Building on (Staffell 2017) and (Khan, Jack, and Stephenson 2018), we are interested in GHG emissions from UK electricity generation over time. We are especially interested in how this might change during the UK covid-19 lockdown period (from ).
Several articles by ythe UK ESO and others have already explored this topic:
Fortunately the UK Electricity System Operator publishes half-hourly generation data which includes both power generation (and thus 'demand') by fuels and also includes a per-kWh carbon intensity for the electricity produced per half-hour. We use this data to explore the following research questions:
To what extent has electricty demand shown deviation from 'normal' demand patterns during the lockdown period?
Has the composition of fuel sources supplying electriicty changed during this period?
Has the lockdown changed greenhouse-gas emissions associated with electriicty generation?
This is 'grid' generation from major power stations of various kinds. Data downloaded from https://data.nationalgrideso.com/carbon-intensity1/historic-generation-mix/r/historic_gb_generation_mix
and pre-processed.
| DATETIME | year | rDateTimeUTC | GENERATION | CARBON_INTENSITY |
|---|---|---|---|---|
| 2016-01-01 00:00:00 | 2016 | 2016-01-01 00:00:00 | 28789 | 249 |
| 2016-01-01 00:30:00 | 2016 | 2016-01-01 00:30:00 | 29372 | 262 |
| 2016-01-01 01:00:00 | 2016 | 2016-01-01 01:00:00 | 29468 | 275 |
| 2016-01-01 01:30:00 | 2016 | 2016-01-01 01:30:00 | 28783 | 277 |
| 2016-01-01 02:00:00 | 2016 | 2016-01-01 02:00:00 | 27736 | 278 |
| 2016-01-01 02:30:00 | 2016 | 2016-01-01 02:30:00 | 27045 | 277 |
Note that according to the dataset source:
mean MW
over the half hour or it may mean MWh per half-hour
. It is unclear. The % refers to the fuel mix. Which one could easily calculate from the MW values. But anyway...Table 2.2 shows the mean half hourly generation (MW) and mean carbon intensity over the years covered by the data. It also shows the implied mean half-hourly total kg CO2e per half hour which we have calculated as follows: {#kgcalc} * convert GENERATION
(MW) to MWh per half hour by GENERATION/2
(1 MW for half an hour = 1/2 MWh) * convert the result to kWh (* 10000
) * multiply by the CARBON_INTENSITY
which is in gCO2e/kWh * divide by 1000 to get Kg
Yes, we have *1000 and then /1000 which is 1 but for the sake of clarity we have kept all the steps.
NB: we are unclear how generation via interconnect is included in the original carbon intensity calculation but note that the ESO forecast methodology document states that it is.
| year | Total annual CO2e (MtCO2e, DBEIS) | Total annual CO2e (MtCO2e, calculated) | Mean halfhourly CO2e (tCO2e, calculated) | Mean half-hourly carbon intensity (gCO2e/kWh) | Mean halfhourly generation (GWh) | Total annual generation (TWh) | Max date in range | nObs |
|---|---|---|---|---|---|---|---|---|
| 2016 | 83.0 | 92.39 | 5259.25 | 299.11 | 34.12 | 299.69 | 2016-12-31 | 17568 |
| 2017 | 73.1 | 81.19 | 4634.19 | 262.77 | 33.95 | 297.37 | 2017-12-31 | 17520 |
| 2018 | 66.8 | 72.13 | 4117.03 | 234.98 | 33.88 | 296.78 | 2018-12-31 | 17520 |
| 2019 | NA | 64.17 | 3662.62 | 214.09 | 33.27 | 291.45 | 2019-12-31 | 17520 |
| 2020 | NA | 24.64 | 3019.20 | 185.73 | 31.69 | 129.28 | 2020-06-18 | 8160 |
As we can see mean half-hourly generation has declined over the years but much less spectacularly than the mean carbon intensity which decreased by 57 from 2017 to 2020.
Table of peak periods
| year | Early morning | Morning peak | Day time | Evening peak | Late evening | Total |
|---|---|---|---|---|---|---|
| 2016 | 5.85 | 1.62 | 8.80 | 4.83 | 3.88 | 24.97 |
| 2017 | 5.78 | 1.61 | 8.74 | 4.80 | 3.85 | 24.78 |
| 2018 | 5.78 | 1.61 | 8.75 | 4.77 | 3.82 | 24.73 |
| 2019 | 5.69 | 1.58 | 8.59 | 4.66 | 3.77 | 24.29 |
| 2020 | 5.08 | 1.36 | 7.56 | 4.16 | 3.39 | 21.55 |
Fuel trend plots by peak period
## `geom_smooth()` using formula 'y ~ x'
## Saving 10 x 5 in image
## `geom_smooth()` using formula 'y ~ x'
Repeat using openair::smoothTrend() for de-seasoning and other benefits. NB: deaseasoning produces -ve values for coal :-)
## png
## 2
Figure @(fig:tilePlotGW): Tile plot of halfhourly GW over time. We like tile plots.
## Saving 9 x 5 in image
Figure @(fig:profilePlotGW): line plot of halfhourly GW by season.
## Saving 9 x 5 in image
## Saving 9 x 5 in image
Figure @(fig:tilePlotCI): Tile plot of halfhourly CI over time. We like tile plots
## Saving 9 x 5 in image
Figure @(fig:tilePlotCO2e): Tile plot of halfhourly total CO2e (kT) over time. We like tile plots
## Saving 9 x 5 in image
Figure @(fig:GWhBySeason2019): halfhourly mean GWh by season. We like tile plots
## Saving 9 x 5 in image
Essentially 'non-grid' generation from solar photovoltaic and small scale wind which is 'embedded' - i.e. non-grid connected as it is connected 'downstream' of the grid exit points. We are not entirely sure if this is accounted for in the grid dataset or not.
We have not yet found a source of this data (if we even need it).
For now embedded generation data is probably not included in the following analysis but we would expect it to depress grid demand when there is greatest insolation (middle of the day, obvs) and wind (largely random in the UK?).
In this section we analyse changes in electricity demand and associated carbon emissions during the UK Covid-19 lockdown via analysis of the generation data.
Several articles and analyses have suggested that demand (and thus generation) patterns have shifted so that weekdays have become more like weekends.
Figure 3.1 uses the openair TheilSen()
function (Carslaw and Ropkins 2012) to create a de-seasoned trend plot for mean monthly half-hourly (i.e. the mean of all half-hourly GW values for each month) generation in GW split by peak period (07:00 - 08:30 and 16:00 - 20:00) .
These plots show that there has been a steady decline of ~ 0.4 - 0.5 GW mean half hourly generation (and thus demand) per year since 2016 in each time period. The substantial below-trend fall in generation during lockdown in early Spring 2020 is clear, especailly for the morning peak, day-time and, to a lesser extent, evening peak periods. There appears to have been a smaller below-trend reduction at other times as we would expect. We can also see a notable drop in generation in late 2015 which was unusually warm and wet.
use mean of half-hourly GWh otherwise the periods with more half-hours tend to visually dominate
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## png
## 2
| year | Early morning | Morning peak | Day time | Evening peak | Late evening |
|---|---|---|---|---|---|
| 2016 | 13.69 | 17.71 | 19.24 | 19.78 | 15.89 |
| 2017 | 13.58 | 17.60 | 19.17 | 19.72 | 15.82 |
| 2018 | 13.58 | 17.62 | 19.18 | 19.58 | 15.71 |
| 2019 | 13.35 | 17.31 | 18.82 | 19.15 | 15.51 |
| 2020 | 12.80 | 15.97 | 17.78 | 18.37 | 14.96 |
Use the openair results object to estimate what the 2020 values would have been if the deaseasoned trend slope had continued.
need to use the Theil-Sen slope model to estimate what the values would have been
## Saving 7 x 5 in image
| date | diff_Early morning | diff_Morning peak | diff_Day time | diff_Evening peak | diff_Late evening | diffTotal |
|---|---|---|---|---|---|---|
| Jan 2020 | -0.10 | -0.77 | -0.13 | -0.55 | -0.35 | -1.91 |
| Feb 2020 | -0.18 | -0.44 | -0.19 | -0.65 | -0.30 | -1.76 |
| Mar 2020 | -0.19 | -0.57 | -0.26 | -0.31 | -0.27 | -1.59 |
| Apr 2020 | -1.95 | -3.57 | -2.88 | -2.08 | -1.58 | -12.07 |
| May 2020 | -1.27 | -2.61 | -2.20 | -1.68 | -1.11 | -8.88 |
| Jun 2020 | -0.65 | -1.30 | -1.34 | -0.96 | -0.66 | -4.92 |
| date | pcDiff_Early morning | pcDiff_Morning peak | pcDiff_Day time | pcDiff_Evening peak | pcDiff_Late evening |
|---|---|---|---|---|---|
| Jan 2020 | -0.80 | -4.51 | -0.67 | -2.89 | -2.31 |
| Feb 2020 | -1.35 | -2.55 | -1.02 | -3.43 | -1.99 |
| Mar 2020 | -1.47 | -3.32 | -1.39 | -1.63 | -1.74 |
| Apr 2020 | -14.89 | -20.92 | -15.52 | -10.99 | -10.38 |
| May 2020 | -9.70 | -15.31 | -11.86 | -8.90 | -7.33 |
| Jun 2020 | -4.99 | -7.62 | -7.25 | -5.11 | -4.35 |
Theil-Sen by weekday (for fun):
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
Figure 3.2 shows total half-hourly generation since 2020-02-01. Overall generation has fallen as we would expect given the season (less heating and lighting required) and weekdays are indeed less easy to distinguish from weekends.
## `geom_smooth()` using formula 'y ~ x'
Figure 3.3 shows the daily generation profiles over time for each day of the week. Clearly the shapes are both reducing in magnitude (seasonal and lockdown effects) and also converging in shape.
Figure 3.4 shows the mean half-hourly GWh per day. Need to bear in mind the trends identified above.
## Saving 9 x 5 in image
Figure 3.5 shows the mean half-hourly CO2e per half-hour of each day. This plot shows the values as a proportion of the sum of the means across all half hours of each day to illustrate the relative, rather than absolute, shift in generation.
## Saving 9 x 5 in image
Figure 3.6 shows the difference between hourly and weekday patterns for lockdown 2020 and the same period in all years from 2018 onwards. It is interesting to note that the twin peak demand periods have been maintained during lockdown but they are considerably lower although we should expect some 'natural' reduction due to the overall downward generation trend shown in Figure 3.1. The lower middle monthly plot suggests that while lockdown caused a considerable drop in generation, this difference is now starting to converge on 'normal' seasonal demand levels.
## Warning: removing 192 missing rows due to plotPeriodDetailed
Repeat but less detail.
## Confirming dates plotted: date (as set for openair)
## Min. 1st Qu. Median Mean 3rd Qu.
## "2017-01-05 00:00:00" "2017-06-25 23:52:30" "2018-06-23 23:45:00" "2018-09-25 19:46:23" "2019-06-21 23:37:30"
## Max.
## "2020-06-18 23:30:00"
## Confirming dates plotted: dateFixed
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2020-01-02" "2020-02-13" "2020-03-27" "2020-03-27" "2020-05-09" "2020-06-22"
## Confirming months plotted: date
##
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 5472 5424 5952 5760 5952 4464 0 0 0 0 0 0
## png
## 2
## png
## 2
## png
## 2
Figure 3.8 is a similar plot but in this case the generation is normalised within each period to enable clarification of changes in the temporal shape of demand rather than changes in the overall level. As we can see the normalised plots indicate that the relative temporal distribution of generation (demand) is very similar between each period. The shape of demand is therefore relatively unchanged, but it's level is distinctly lower (Figure 3.7).
For this analysis we have shifted the dates for the comparison years to ensure that weekdays and weekends align in each year of data but this does not mean that Easter is the same weekend across the comparison periods nor that Bank Holidays (e.g. VE day 2020) are consistent.
Figure 3.9 shows the most recent mean daily half-hourly GW compared to the same day in previous years. As hinted by Figure 3.7, lockdown clearly reduced demand below the seasonal trend as Figure 3.1 suggested and also levels of demand are now starting to converge on the seasonal trend (c.f. Figure 3.7).
Beware temperature differences - need to control for heating degree days as 2020 was a very warm spring
## Warning: Ignoring unknown parameters: yMin, yMax
## Saving 9 x 5 in image
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Figure 3.10 shows the percentage difference between the mean half-hourly generation per day in 2020 and the pre 2020 average for the same day. As we can see January 2020 was already slightly lower than previous years but February appears similar. There appears to be a substantial lockdown effect albiet with some fluctuations and very large dips on VE Day (Friday 8th May) and Thursday 28th May.
## Warning: Ignoring unknown parameters: yMin, yMax
## Saving 9 x 5 in image
Figure 3.11 shows the trend in the ratio between maximum and minimum half-hourly generation by day. This shows that 2020 was already less 'peaky' than previous years with a lower ratio between maximum and minimum generation on most days. This appear to have amplified in the first week or so of UK lockdown with a further reduction in the ratio but this has not been sustained over time with greater variance as we move into May 2020.
## `geom_smooth()` using formula 'y ~ x'
tbc
There are two aspects to this. The first is carbon intensity
which is driven by the mix of fuels being used to generate electricity at any given time. The second is the total greenhouse gasses emitted which is, obviously, the intensity * the volume. Given the slight uncertainty over units (see Section 2) we assume this is GENERATION
* CARBON_INTENSITY
.
Clearly the first of these is driven by the mix of fuels and in the UK this reflects a complex dynamic system of availability of renewables, price, interconnect and demand. To some extent we would expect that lower overall demand should (but not always) increase the share of renewables. However other factors are also at play:
Figure 3.12 agin uses the openair TheilSen()
function (Carslaw and Ropkins 2012) to create a de-seasoned trend plot but this time for mean half-hourly carbon intensity by peak demand period. Again Table 3.3 indicates the years over which the trend is calculated. This plot shows that carbon intensity has fallen by around 40 kg CO2e/MWh per year over the time period in all time periods. However it is also noticeable that mean carbon intensity has marginally increased above trend in the months of lockdown.
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
| year | Early morning | Morning peak | Day time | Evening peak | Late evening |
|---|---|---|---|---|---|
| 2016 | 278.36 | 312.40 | 298.02 | 329.20 | 302.41 |
| 2017 | 243.26 | 274.14 | 261.92 | 290.35 | 266.69 |
| 2018 | 210.10 | 247.87 | 237.46 | 269.08 | 234.98 |
| 2019 | 190.84 | 227.23 | 217.58 | 241.80 | 215.58 |
| 2020 | 168.52 | 193.40 | 181.59 | 213.96 | 192.48 |
## png
## 2
Use the openair results object to estimate what the 2020 values would have been if the deaseasoned trend slope had continued.
## Saving 7 x 5 in image
| date | diff_Early morning | diff_Morning peak | diff_Day time | diff_Evening peak | diff_Late evening | diffTotal |
|---|---|---|---|---|---|---|
| Jan 2020 | 4.10 | 3.21 | -3.02 | -14.87 | -0.47 | -11.05 |
| Feb 2020 | -27.51 | -39.55 | -51.99 | -39.92 | -31.87 | -190.84 |
| Mar 2020 | 3.53 | 11.79 | 8.22 | -3.51 | 0.08 | 20.11 |
| Apr 2020 | 18.98 | 5.22 | -1.38 | 9.95 | 17.19 | 49.96 |
| May 2020 | 16.76 | 4.81 | 1.35 | 13.11 | 18.37 | 54.39 |
| Jun 2020 | 37.88 | 24.79 | 35.04 | 43.59 | 41.70 | 182.99 |
| date | pcDiff_Early morning | pcDiff_Morning peak | pcDiff_Day time | pcDiff_Evening peak | pcDiff_Late evening |
|---|---|---|---|---|---|
| Jan 2020 | 2.50 | 1.62 | -1.58 | -6.76 | -0.25 |
| Feb 2020 | -17.04 | -20.16 | -27.46 | -18.35 | -16.93 |
| Mar 2020 | 2.22 | 6.08 | 4.39 | -1.63 | 0.04 |
| Apr 2020 | 12.11 | 2.73 | -0.75 | 4.67 | 9.37 |
| May 2020 | 10.86 | 2.55 | 0.74 | 6.23 | 10.14 |
| Jun 2020 | 24.95 | 13.30 | 19.44 | 20.96 | 23.34 |
Theil-Sen by weekday (for fun):
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
Figure 3.13 shows half-hourly carbon intensity since 2020-02-01. Overall generation has fallen as we would expect given the season (less heating and lighting required) and weekdays are indeed less easy to distinguish from weekends.
## `geom_smooth()` using formula 'y ~ x'
Figure 3.14 shows the daily carbon intensity profiles over time for each day of the week.
Figure 3.15 shows the mean half-hourly CO2e per day. Need to bear in mind the trends identified above.
## Saving 9 x 5 in image
Figure 3.16 shows the mean half-hourly CO2e per half-hour of each day. This plot shows the values as a proportion of the sum of the means across all half hours of each day to illustrate the relative, rather than absolute, shift in generation.
## Saving 9 x 5 in image
Figure 3.17 shows the difference between hourly and weekday patterns for lockdown 2020 and the previous year(s) starting from January 2018. This plot is not particularly informative since we now CI is already lower in 2020 than previous years and we would expect it to fall during the spring as solar generation increases its contributon. The lockdown phases will also be affected by small numbers of highly windy days.
## Confirming dates plotted: date (as set for openair)
## Min. 1st Qu. Median Mean 3rd Qu.
## "2017-01-05 00:00:00" "2017-06-25 23:52:30" "2018-06-23 23:45:00" "2018-09-25 19:46:23" "2019-06-21 23:37:30"
## Max.
## "2020-06-18 23:30:00"
## Confirming dates plotted: dateFixed
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2020-01-02" "2020-02-13" "2020-03-27" "2020-03-27" "2020-05-09" "2020-06-22"
## Confirming months plotted: date
##
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 5472 5424 5952 5760 5952 4464 0 0 0 0 0 0
## png
## 2
## png
## 2
## png
## 2
Figure 3.18 shows the mean half-hourly carbon intensity per day in 2020 and the pre 2020 average for the same day. As we would expect given Figure 3.12, 2020 was already considerably lower than the average of previous years but this is not necessarily sustained through lockdown although the affects of weather on solar and wind availability need to be taken in to account.
## Warning: Ignoring unknown parameters: yMin, yMax
## Saving 9 x 5 in image
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Figure 3.19 shows the percentage difference between the mean half-hourly carbon intensity per day in 2020 and the pre 2020 average for the same day. As expected, 2020 was already considerably lower than the average of previous years but this is not necessarily sustained through lockdown although the affects of weather on solar and wind availability need to be taken in to account.
## Warning: Ignoring unknown parameters: yMin, yMax
## Saving 9 x 5 in image
tbc
In this section we use our calculation of total CO2e emitted per half hour (see Section 2.1) to analyse the changes in total CO2e emitted which is, after all, what we are mostly interested in from a climate change point of view. Remember that this value is driven both by total generation (demand) and carbon intensity. As we saw above, these are not always tightly correlated.
Figure 3.20 again uses the openair TheilSen()
function (Carslaw and Ropkins 2012) to create a de-seasoned trend plot but this time for mean half-hourly carbon intensity by peak demnand period. Again Table 3.5 indicates the years over which the trend is calculated.
This plot shows that total CO2e emissions intensity has fallen by a half-hourly mean of ~600-800 tonnes of CO2e over the time period. However there is some variation around this trend with apparently slightly above trend emissions in the evening peak period during April and May 2020 noticeable despite the almost complete absence of coal generation during this period (see Annex, Figure ??).
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## png
## 2
| year | Early morning | Morning peak | Day time | Evening peak | Late evening |
|---|---|---|---|---|---|
| 2016 | 3894.87 | 5666.75 | 5864.67 | 6675.04 | 4943.11 |
| 2017 | 3396.89 | 4975.10 | 5184.76 | 5901.14 | 4372.38 |
| 2018 | 2911.83 | 4473.25 | 4664.49 | 5387.04 | 3796.03 |
| 2019 | 2579.85 | 4020.44 | 4181.45 | 4702.25 | 3410.86 |
| 2020 | 2165.19 | 3180.52 | 3315.67 | 3992.39 | 2924.12 |
Now use the openair results object to estimate what the 2020 values would have been if the deaseasoned trend slope had continued.
## Saving 7 x 5 in image
| date | diff_Early morning | diff_Morning peak | diff_Day time | diff_Evening peak | diff_Late evening | diffTotal |
|---|---|---|---|---|---|---|
| Jan 2020 | 41.73 | -124.90 | -274.08 | -650.80 | -168.21 | -1176.25 |
| Feb 2020 | -531.72 | -810.28 | -1477.64 | -1047.19 | -753.59 | -4620.42 |
| Mar 2020 | 59.63 | -80.51 | 143.78 | -255.17 | -154.39 | -286.66 |
| Apr 2020 | 27.13 | -421.38 | -339.92 | 42.41 | 110.29 | -581.47 |
| May 2020 | 152.97 | -230.03 | -141.18 | 237.17 | 235.54 | 254.48 |
| Jun 2020 | 494.74 | 302.36 | 490.63 | 807.63 | 617.60 | 2712.96 |
| date | pcDiff_Early morning | pcDiff_Morning peak | pcDiff_Day time | pcDiff_Evening peak | pcDiff_Late evening |
|---|---|---|---|---|---|
| Jan 2020 | 1.97 | -3.60 | -7.52 | -15.57 | -5.71 |
| Feb 2020 | -25.60 | -23.72 | -41.13 | -25.41 | -25.98 |
| Mar 2020 | 2.92 | -2.39 | 4.06 | -6.28 | -5.40 |
| Apr 2020 | 1.36 | -12.72 | -9.73 | 1.06 | 3.92 |
| May 2020 | 7.79 | -7.05 | -4.10 | 6.01 | 8.51 |
| Jun 2020 | 25.73 | 9.42 | 14.45 | 20.77 | 22.70 |
Theil-Sen by weekday (for fun):
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
## [1] "Taking bootstrap samples. Please wait."
Figure 3.21 shows total half-hourly CO2e emissions since 2020-02-01. Overall generation has fallen as we would expect given the season (less heating and lighting required) and weekdays are indeed less easy to distinguish from weekends.
## `geom_smooth()` using formula 'y ~ x'
Figure 3.22 shows the daily CO2e emissions profiles over time for each day of the week since 2020-02-01.
Figure 3.23 shows the mean half-hourly CO2e per day. Need to bear in mind the trends identified above.
## Saving 9 x 5 in image
Figure 3.24 shows the mean half-hourly CO2e per half-hour of each day. This plot shows the values as a proportion of the sum of the means across all half hours of each day to illustrate the relative, rather than absolute, shift in generation.
## Saving 9 x 5 in image
Figure 3.25 shows the difference between hourly and weekday patterns for lockdown 2020 and the previous year(s) starting from January 2018. Again, due to the downward trends we have already identified, we would expect total CO2e emissions to be lower during 2020 and also to fall during the spring as solar generation increases its contributon.
## Confirming dates plotted: date (as set for openair)
## Min. 1st Qu. Median Mean 3rd Qu.
## "2017-01-05 00:00:00" "2017-06-25 23:52:30" "2018-06-23 23:45:00" "2018-09-25 19:46:23" "2019-06-21 23:37:30"
## Max.
## "2020-06-18 23:30:00"
## Confirming dates plotted: dateFixed
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2020-01-02" "2020-02-13" "2020-03-27" "2020-03-27" "2020-05-09" "2020-06-22"
## Confirming months plotted: date
##
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 5472 5424 5952 5760 5952 4464 0 0 0 0 0 0
## png
## 2
## png
## 2
## png
## 2
Figure 3.26 shows the mean half-hourly carbon intensity per day in 2020 and the pre 2020 average for the same day. As expected, 2020 was already considerably lower than the average of previous years but this is not necessarily sustained through lockdown although the affects of weather on solar and wind availability need to be taken in to account.
check y axis
## Warning: Ignoring unknown parameters: yMin, yMax
## Saving 9 x 5 in image
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Figure 3.27 shows the percentage difference between the mean half-hourly carbon intensity per day in 2020 and the pre 2020 average for the same day. As expected, 2020 was already considerably lower than the average of previous years but this is not necessarily sustained through lockdown although the affects of weather on solar and wind availability need to be taken in to account.
## Warning: Ignoring unknown parameters: yMin, yMax
## Saving 9 x 5 in image
tbc
If you wish to use any of the material from this report please cite as:
This work is (c) 2020 the authors. Usage rights are specified in the License section (5.4).
All code used to create this report is available from:
This work is made available under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License.
This means you are free to:
Under the following terms:
Notices:
For the avoidance of doubt and explanation of terms please refer to the full license notice and legal code.
You may not be reading the most recent version of this report. Please check:
We do not 'support' the code but if you notice a problem please check the issues on our repo and if it doesn't already exist, please open a new one. * this report's edit history
This work was supported by:
Descriptive stats using skimr::skim():
| Name | gridGenDT |
| Number of rows | 78288 |
| Number of columns | 50 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| Date | 2 |
| difftime | 1 |
| factor | 3 |
| numeric | 41 |
| POSIXct | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| rDateTime | 0 | 1 | 20 | 20 | 0 | 78288 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| obsDate | 0 | 1 | 2016-01-01 | 2020-06-18 | 2018-03-26 | 1631 |
| date | 0 | 1 | 2016-01-01 | 2020-06-18 | 2018-03-26 | 1631 |
Variable type: difftime
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| hms | 0 | 1 | 0 secs | 84600 secs | 42300 secs | 48 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| peakPeriod | 0 | 1 | FALSE | 5 | Day: 24465, Ear: 22834, Eve: 13048, Lat: 13048 |
| season | 0 | 1 | FALSE | 4 | Spr: 22080, Win: 20208, Sum: 18528, Aut: 17472 |
| dow | 0 | 1 | TRUE | 7 | Sun: 11184, Mon: 11184, Tue: 11184, Wed: 11184 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| GAS | 0 | 1 | 1.317777e+04 | 5.265370e+03 | 2291.00 | 9.11475e+03 | 1.292500e+04 | 1.715200e+04 | 2.747200e+04 | ▃▇▇▅▁ |
| COAL | 0 | 1 | 1.866260e+03 | 2.380300e+03 | 0.00 | 2.31000e+02 | 8.830000e+02 | 2.594000e+03 | 1.472400e+04 | ▇▂▁▁▁ |
| NUCLEAR | 0 | 1 | 6.845520e+03 | 1.022700e+03 | 3705.00 | 6.21300e+03 | 6.943000e+03 | 7.646000e+03 | 8.859000e+03 | ▁▂▆▇▃ |
| WIND | 0 | 1 | 5.313530e+03 | 3.702440e+03 | 0.00 | 2.29800e+03 | 4.554000e+03 | 7.632000e+03 | 1.712900e+04 | ▇▆▃▂▁ |
| HYDRO | 0 | 1 | 4.152200e+02 | 2.541200e+02 | 0.00 | 2.08000e+02 | 3.810000e+02 | 5.860000e+02 | 1.403000e+03 | ▇▇▅▁▁ |
| IMPORTS | 0 | 1 | 2.529940e+03 | 9.569900e+02 | 0.00 | 1.95900e+03 | 2.720000e+03 | 3.124000e+03 | 4.884000e+03 | ▂▅▇▇▂ |
| BIOMASS | 0 | 1 | 1.134800e+03 | 1.060460e+03 | 0.00 | 0.00000e+00 | 1.295000e+03 | 2.078000e+03 | 3.204000e+03 | ▇▂▃▅▂ |
| OTHER | 0 | 1 | 7.295900e+02 | 8.226500e+02 | 0.00 | 8.90000e+01 | 1.420000e+02 | 1.462000e+03 | 2.173000e+03 | ▇▁▁▂▃ |
| SOLAR | 0 | 1 | 1.306980e+03 | 2.018680e+03 | 0.00 | 0.00000e+00 | 1.300000e+01 | 2.130000e+03 | 9.680000e+03 | ▇▂▁▁▁ |
| STORAGE | 0 | 1 | 2.632100e+02 | 3.273800e+02 | 0.00 | 0.00000e+00 | 1.700000e+02 | 3.900000e+02 | 2.394000e+03 | ▇▁▁▁▁ |
| GENERATION | 0 | 1 | 3.358283e+04 | 7.038010e+03 | 18287.00 | 2.81860e+04 | 3.331400e+04 | 3.849250e+04 | 5.342700e+04 | ▃▇▇▅▁ |
| CARBON_INTENSITY | 0 | 1 | 2.457800e+02 | 7.526000e+01 | 54.00 | 1.95000e+02 | 2.410000e+02 | 2.910000e+02 | 5.110000e+02 | ▂▇▇▂▁ |
| LOW_CARBON | 0 | 1 | 1.501605e+04 | 4.152520e+03 | 6755.00 | 1.18200e+04 | 1.449700e+04 | 1.779925e+04 | 3.074600e+04 | ▅▇▅▂▁ |
| ZERO_CARBON | 0 | 1 | 1.388126e+04 | 3.849980e+03 | 4852.00 | 1.08580e+04 | 1.350200e+04 | 1.652800e+04 | 2.847300e+04 | ▂▇▆▂▁ |
| RENEWABLE | 0 | 1 | 7.035740e+03 | 4.049150e+03 | 125.00 | 3.82300e+03 | 6.496000e+03 | 9.709250e+03 | 2.311800e+04 | ▇▇▅▁▁ |
| FOSSIL | 0 | 1 | 1.504403e+04 | 6.709900e+03 | 2421.00 | 9.94300e+03 | 1.429050e+04 | 1.924000e+04 | 3.684200e+04 | ▅▇▆▂▁ |
| GAS_perc | 0 | 1 | 3.840000e+01 | 1.094000e+01 | 6.60 | 3.09000e+01 | 3.990000e+01 | 4.680000e+01 | 7.010000e+01 | ▁▅▇▆▁ |
| COAL_perc | 0 | 1 | 4.920000e+00 | 5.630000e+00 | 0.00 | 8.00000e-01 | 2.800000e+00 | 7.200000e+00 | 3.020000e+01 | ▇▂▁▁▁ |
| NUCLEAR_perc | 0 | 1 | 2.128000e+01 | 5.530000e+00 | 9.70 | 1.72000e+01 | 2.050000e+01 | 2.460000e+01 | 4.310000e+01 | ▃▇▅▁▁ |
| WIND_perc | 0 | 1 | 1.620000e+01 | 1.142000e+01 | 0.00 | 7.00000e+00 | 1.375000e+01 | 2.300000e+01 | 5.830000e+01 | ▇▆▃▂▁ |
| HYDRO_perc | 0 | 1 | 1.210000e+00 | 6.700000e-01 | 0.00 | 7.00000e-01 | 1.100000e+00 | 1.700000e+00 | 4.200000e+00 | ▇▇▅▁▁ |
| IMPORTS_perc | 0 | 1 | 7.890000e+00 | 3.320000e+00 | 0.00 | 5.70000e+00 | 8.100000e+00 | 1.030000e+01 | 1.890000e+01 | ▂▆▇▃▁ |
| BIOMASS_perc | 0 | 1 | 3.500000e+00 | 3.370000e+00 | 0.00 | 0.00000e+00 | 3.800000e+00 | 6.200000e+00 | 1.610000e+01 | ▇▅▃▁▁ |
| OTHER_perc | 0 | 1 | 2.230000e+00 | 2.550000e+00 | 0.00 | 3.00000e-01 | 5.000000e-01 | 4.600000e+00 | 1.050000e+01 | ▇▂▂▁▁ |
| SOLAR_perc | 0 | 1 | 3.680000e+00 | 5.810000e+00 | 0.00 | 0.00000e+00 | 0.000000e+00 | 5.700000e+00 | 3.280000e+01 | ▇▂▁▁▁ |
| STORAGE_perc | 0 | 1 | 7.000000e-01 | 8.500000e-01 | 0.00 | 0.00000e+00 | 5.000000e-01 | 1.100000e+00 | 7.900000e+00 | ▇▁▁▁▁ |
| GENERATION_perc | 0 | 1 | 1.000000e+02 | 0.000000e+00 | 100.00 | 1.00000e+02 | 1.000000e+02 | 1.000000e+02 | 1.000000e+02 | ▁▁▇▁▁ |
| LOW_CARBON_perc | 0 | 1 | 4.586000e+01 | 1.309000e+01 | 16.40 | 3.61000e+01 | 4.420000e+01 | 5.420000e+01 | 8.790000e+01 | ▂▇▆▂▁ |
| ZERO_CARBON_perc | 0 | 1 | 4.236000e+01 | 1.212000e+01 | 15.50 | 3.33000e+01 | 4.080000e+01 | 5.000000e+01 | 8.510000e+01 | ▂▇▅▂▁ |
| RENEWABLE_perc | 0 | 1 | 2.108000e+01 | 1.194000e+01 | 0.50 | 1.17000e+01 | 1.930000e+01 | 2.860000e+01 | 6.620000e+01 | ▆▇▅▂▁ |
| FOSSIL_perc | 0 | 1 | 4.331000e+01 | 1.293000e+01 | 9.00 | 3.43000e+01 | 4.420000e+01 | 5.270000e+01 | 7.670000e+01 | ▂▅▇▆▁ |
| year | 0 | 1 | 2.017760e+03 | 1.310000e+00 | 2016.00 | 2.01700e+03 | 2.018000e+03 | 2.019000e+03 | 2.020000e+03 | ▇▇▇▇▃ |
| nHalfHours | 0 | 1 | 4.800000e+01 | 0.000000e+00 | 48.00 | 4.80000e+01 | 4.800000e+01 | 4.800000e+01 | 4.800000e+01 | ▁▁▇▁▁ |
| GENERATION_MW | 0 | 1 | 3.358283e+04 | 7.038010e+03 | 18287.00 | 2.81860e+04 | 3.331400e+04 | 3.849250e+04 | 5.342700e+04 | ▃▇▇▅▁ |
| GW | 0 | 1 | 3.358000e+01 | 7.040000e+00 | 18.29 | 2.81900e+01 | 3.331000e+01 | 3.849000e+01 | 5.343000e+01 | ▃▇▇▅▁ |
| GENERATION_MWh | 0 | 1 | 1.679142e+04 | 3.519000e+03 | 9143.50 | 1.40930e+04 | 1.665700e+04 | 1.924625e+04 | 2.671350e+04 | ▃▇▇▅▁ |
| GWh | 0 | 1 | 1.679000e+01 | 3.520000e+00 | 9.14 | 1.40900e+01 | 1.666000e+01 | 1.925000e+01 | 2.671000e+01 | ▃▇▇▅▁ |
| C02e_g | 0 | 1 | 4.272960e+09 | 1.990252e+09 | 776697500.00 | 2.81762e+09 | 3.915386e+09 | 5.280662e+09 | 1.274796e+10 | ▆▇▃▁▁ |
| C02e_kg | 0 | 1 | 4.272960e+06 | 1.990252e+06 | 776697.50 | 2.81762e+06 | 3.915386e+06 | 5.280662e+06 | 1.274796e+07 | ▆▇▃▁▁ |
| C02e_T | 0 | 1 | 4.272960e+03 | 1.990250e+03 | 776.70 | 2.81762e+03 | 3.915390e+03 | 5.280660e+03 | 1.274796e+04 | ▆▇▃▁▁ |
| month | 0 | 1 | 6.190000e+00 | 3.450000e+00 | 1.00 | 3.00000e+00 | 6.000000e+00 | 9.000000e+00 | 1.200000e+01 | ▇▆▅▅▆ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| DATETIME | 0 | 1 | 2016-01-01 | 2020-06-18 23:30:00 | 2018-03-26 11:45:00 | 78288 |
| rDateTimeUTC | 0 | 1 | 2016-01-01 | 2020-06-18 23:30:00 | 2018-03-26 11:45:00 | 78288 |
Descriptive stats using skimr::skim():
| Name | embeddedGenDT |
| Number of rows | 92458 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| Date | 1 |
| difftime | 1 |
| factor | 1 |
| numeric | 7 |
| POSIXct | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| rDate | 0 | 1 | 10 | 10 | 0 | 1926 | 0 |
| FORECAST_ACTUAL_INDICATOR | 0 | 1 | 0 | 1 | 90058 | 2 | 0 |
| rDateTime | 0 | 1 | 20 | 20 | 0 | 92448 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| obsDate | 0 | 1 | 2015-01-01 | 2020-06-19 | 2017-08-24 | 1926 |
Variable type: difftime
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| hms | 0 | 1 | 0 secs | 84600 secs | 41400 secs | 48 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| peakPeriod | 0 | 1 | FALSE | 5 | Day: 28890, Ear: 26974, Eve: 15408, Lat: 15408 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| SETTLEMENT_PERIOD | 0 | 1 | 24.50 | 13.86 | 1 | 13 | 25 | 37 | 50 | ▇▇▇▇▆ |
| EMBEDDED_WIND_GENERATION | 0 | 1 | 1278.77 | 679.44 | 111 | 729 | 1174 | 1726 | 3826 | ▇▇▅▂▁ |
| EMBEDDED_WIND_CAPACITY | 0 | 1 | 5205.23 | 889.72 | 3601 | 4253 | 5305 | 5978 | 6527 | ▇▃▇▆▇ |
| EMBEDDED_SOLAR_GENERATION | 0 | 1 | 1189.84 | 1853.63 | 0 | 0 | 29 | 1890 | 9670 | ▇▂▁▁▁ |
| EMBEDDED_SOLAR_CAPACITY | 0 | 1 | 11507.12 | 1860.90 | 5994 | 9602 | 12372 | 13052 | 13080 | ▁▁▂▂▇ |
| year | 0 | 1 | 2017.16 | 1.53 | 2015 | 2016 | 2017 | 2018 | 2020 | ▇▃▃▃▁ |
| nHalfHours | 0 | 1 | 48.00 | 0.00 | 48 | 48 | 48 | 48 | 48 | ▁▁▇▁▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| DateTime | 0 | 1 | 2015-01-01 | 2020-06-19 23:30:00 | 2017-08-24 00:15:00 | 92448 |
| rDateTimeUTC | 0 | 1 | 2015-01-01 | 2020-06-19 23:30:00 | 2017-08-24 00:15:00 | 92448 |
## Forecast or actual or NA?
##
## A <NA>
## 90058 2400 0
Figure @(fig:tileEmbeddedSolar): Tile plot of halfhourly GW over time. We like tile plots.
## Saving 9 x 5 in image
## Saving 9 x 5 in image
Figure @(fig:tileEmbeddedWind): Tile plot of halfhourly GW over time. We like tile plots.
## Saving 9 x 5 in image
## Saving 9 x 5 in image
So there are data holes.
History: https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer <- up to end March
Updates: https://demandforecast.nationalgrid.com/efs_demand_forecast/demandupdatedownload <- since May 1st
And guess which period we're interested in....
It is not entirely clear if the embedded wind & solar is already included in the gridGen data.
## Using 'meanMW' as value column. Use 'value.var' to override
## year Embedded Solar Grid Solar
## 1: 2016 1089.292 1180.716
## 2: 2017 1187.373 1242.915
## 3: 2018 1317.690 1344.615
## 4: 2019 1331.162 1341.114
## 5: 2020 1400.497 1561.213
## Warning: Removed 3360 rows containing missing values (geom_tile).
2020 only varies because of the data gap in the embedded data files. There seem to be some weird flips. Do we trust this data?
## Using 'meanMW' as value column. Use 'value.var' to override
## year Embedded wind Grid wind
## 1: 2016 1065.739 3015.948
## 2: 2017 1399.284 4614.868
## 3: 2018 1400.371 5622.056
## 4: 2019 1378.099 6637.613
## 5: 2020 1693.090 8254.966
## Warning: Removed 3360 rows containing missing values (geom_tile).
Looks like the wind data is trustable - but we have a gap...
| year | coal | gas | nuclear | wind | hydro | solar | biomass | imports | other | N obs |
|---|---|---|---|---|---|---|---|---|---|---|
| 2016 | 3191.55 | 14486.43 | 7604.86 | 3015.97 | 384.71 | 1180.82 | 0.00 | 2311.30 | 1616.29 | 17568 |
| 2017 | 2353.36 | 13609.80 | 7483.64 | 4614.83 | 452.12 | 1243.04 | 253.72 | 2224.93 | 1397.93 | 17520 |
| 2018 | 1758.89 | 13156.65 | 6923.77 | 5622.07 | 365.88 | 1344.78 | 1838.06 | 2516.04 | 81.70 | 17520 |
| 2019 | 677.69 | 13066.67 | 6023.60 | 6637.58 | 408.03 | 1341.23 | 1963.79 | 2870.51 | 88.51 | 17520 |
| 2020 | 749.65 | 9716.58 | 5437.31 | 8254.97 | 523.11 | 1561.21 | 2179.84 | 2954.13 | 153.14 | 8160 |
Figure ?? shows mean half-hourly generation per month by fuel together with a smoothed trend line. Note that these values are not de-seasoned.
Clearly some of these trends are non-monotonic and so are poorly represented by the Theil-Sen function. The next set of plots repeat this analysis using openair's smoothTrend plot which implemets a GAM-based method similar to 3.9.
Figure 6.21 shows the relationship between half-hourly carbon intensity and half-hourly generation over the last five years. 2020 is excluded as it is not yet complete.
The openair package (Carslaw and Ropkins 2012) contains a number of novel plotting methods.
## Warning: `as_data_frame()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Warning: `data_frame()` is deprecated as of tibble 1.1.0.
## Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
Analysis completed in 304.76 seconds ( 5.08 minutes) using knitr in RStudio with R version 3.6.0 (2019-04-26) running on x86_64-redhat-linux-gnu.
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8 LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] gtools_3.8.1 openair_2.7-2 gapminder_0.3.0 gganimate_1.0.5 zoo_1.8-4 hms_0.5.3
## [7] curl_3.3 lubridate_1.7.9 kableExtra_1.1.0 ggplot2_3.3.1 skimr_2.1.1 drake_7.12.2
## [13] data.table_1.12.0 gridCarbon_0.1.0 here_0.1
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.1 maps_3.3.0 tidyr_1.1.0 jsonlite_1.6 viridisLite_0.3.0 splines_3.6.0
## [7] assertthat_0.2.0 highr_0.7 latticeExtra_0.6-28 base64url_1.4 yaml_2.2.0 progress_1.2.2
## [13] pillar_1.4.4 backports_1.1.3 lattice_0.20-38 glue_1.4.1 digest_0.6.25 RColorBrewer_1.1-2
## [19] rvest_0.3.5 colorspace_1.4-0 htmltools_0.3.6 Matrix_1.2-17 pkgconfig_2.0.2 bookdown_0.19
## [25] purrr_0.3.4 scales_1.0.0 webshot_0.5.2 tweenr_1.0.1 tibble_3.0.1 mgcv_1.8-28
## [31] txtq_0.2.0 generics_0.0.2 farver_2.0.3 ellipsis_0.3.1 withr_2.1.2 repr_1.1.0
## [37] hexbin_1.27.2 cli_2.0.2 magrittr_1.5 crayon_1.3.4 evaluate_0.14 storr_1.2.1
## [43] fansi_0.4.0 nlme_3.1-139 MASS_7.3-51.4 forcats_0.5.0 xml2_1.3.2 tools_3.6.0
## [49] prettyunits_1.0.2 lifecycle_0.2.0 stringr_1.4.0 munsell_0.5.0 cluster_2.0.8 compiler_3.6.0
## [55] rlang_0.4.6 grid_3.6.0 rstudioapi_0.11 igraph_1.2.2 labeling_0.3 base64enc_0.1-3
## [61] rmarkdown_2.2 gtable_0.2.0 R6_2.3.0 knitr_1.28 dplyr_1.0.0 utf8_1.1.4
## [67] filelock_1.0.2 rprojroot_1.3-2 readr_1.3.1 stringi_1.2.4 parallel_3.6.0 Rcpp_1.0.1
## [73] vctrs_0.3.1 mapproj_1.2.7 tidyselect_1.1.0 xfun_0.14
Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for R. https://CRAN.R-project.org/package=rmarkdown.
Carslaw, David C., and Karl Ropkins. 2012. “Openair — an R Package for Air Quality Data Analysis.” Environmental Modelling & Software 27–28 (0): 52–61. doi:10.1016/j.envsoft.2011.09.008.
Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.
Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. http://www.jstatsoft.org/v40/i03/.
Khan, Imran, Michael W. Jack, and Janet Stephenson. 2018. “Analysis of Greenhouse Gas Emissions in Electricity Systems Using Time-Varying Carbon Intensity.” Journal of Cleaner Production 184 (May): 1091–1101. doi:10.1016/j.jclepro.2018.02.309.
Landau, William Michael. 2019. Drake: A Pipeline Toolkit for Reproducible Computation at Scale. https://CRAN.R-project.org/package=drake.
R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Staffell, Iain. 2017. “Measuring the Progress and Impacts of Decarbonising British Electricity.” Energy Policy 102 (March): 463–75. doi:10.1016/j.enpol.2016.12.037.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.
Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.
———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in R. https://CRAN.R-project.org/package=knitr.
Zeileis, Achim, and Gabor Grothendieck. 2005. “Zoo: S3 Infrastructure for Regular and Irregular Time Series.” Journal of Statistical Software 14 (6): 1–27. doi:10.18637/jss.v014.i06.
Zhu, Hao. 2018. KableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.


Wrapper class for the v201008 ReportDefinition service. This class is automatically generated.
| [R] | api | Holds the AdWords::API object to which the wrapper belongs. |
| [R] | module | Holds a shortcut to the parent module. Use this to avoid typing the full class name when creating classes belonging to this service, e.g. service_object.module::ClassName instead of AdWords::V201008::ReportDefinitionService::ClassName This will make it easier to migrate your code between API versions. |
Constructor for ReportDefinitionServiceWrapper.
Args:
- driver: SOAP::RPC::Driver object with the remote SOAP methods for this service
- api: the AdWords::API object to which the wrapper belongs
Source: show
# File lib/adwords4r/v201008/ReportDefinitionServiceWrapper.rb, line 34 def initialize(driver, api) @driver = driver @api = api @module = AdWords::V201008::ReportDefinitionService end
Extension method — Calls the AdWords::Extensions.downloadReport method with self as the first parameter.
Source: show
# File lib/adwords4r/v201008/ReportDefinitionServiceWrapper.rb, line 117 def downloadReport(report_definition_id) return AdWords::Extensions.downloadReport(self, report_definition_id) end
Extension method — Calls the AdWords::Extensions.downloadReportAsFile method with self as the first parameter.
Source: show
# File lib/adwords4r/v201008/ReportDefinitionServiceWrapper.rb, line 123 def downloadReportAsFile(report_definition_id, path) return AdWords::Extensions.downloadReportAsFile(self, report_definition_id, path) end
Calls the get method of the ReportDefinition service. Check the online documentation for this method.
Args:
Returns:
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v201008/ReportDefinitionServiceWrapper.rb, line 52 def get(selector) begin AdWords::Service.validate_param('selector', selector, AdWords::V201008::ReportDefinitionService::ReportDefinitionSelector) # Construct request object and make API call obj = AdWords::V201008::ReportDefinitionService::Get.new(selector) return @driver.get(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "get Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getReportFields method of the ReportDefinition service. Check the online documentation for this method.
Args:
Returns:
- rval: SOAP::SOAPArray of AdWords::V201008::ReportDefinitionService::ReportDefinitionField
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v201008/ReportDefinitionServiceWrapper.rb, line 77 def getReportFields(reportType) begin AdWords::Service.validate_param('reportType', reportType, AdWords::V201008::ReportDefinitionService::ReportDefinitionReportType) # Construct request object and make API call obj = AdWords::V201008::ReportDefinitionService::GetReportFields.new(reportType) return @driver.getReportFields(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getReportFields Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the mutate method of the ReportDefinition service. Check the online documentation for this method.
Args:
- operations: SOAP::SOAPArray of AdWords::V201008::ReportDefinitionService::ReportDefinitionOperation
Returns:
- rval: SOAP::SOAPArray of AdWords::V201008::ReportDefinitionService::ReportDefinition
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v201008/ReportDefinitionServiceWrapper.rb, line 102 def mutate(operations) begin AdWords::Service.validate_param('operations', operations, SOAP::SOAPArray) # Construct request object and make API call obj = AdWords::V201008::ReportDefinitionService::Mutate.new(operations) return @driver.mutate(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "mutate Call Failed: " + fault.faultstring.to_s, caller) end end


Postdoctoral Researcher, Bader Lab
The Donnelly Centre
University of Toronto
160 College Street, Room 630
The Donnelly Centre
University of Toronto
Toronto, Ontario
Canada, M5S 3E1
Map
Email: mohamed.attiashahata[AT]utoronto.ca
Email: helmy.sfc[AT]gmail.com
I am a Postdoc researcher at Bader Lab, The Donnelly Centre of the University of Toronto. During my MSc, Ph.D. and my postdoctoral training at Kyoto University I worked in mathematical modeling of signaling pathways, Bioinformatics/Computational Biology, Proteomics, Systems Biology, Proteogenomics, Cancer Biology, Next-generation Sequence data analysis.
My the current focus is developing novel methods for mutations identification in cancer, studying the disease mutations impact in protein interaction networks and gene regulatory networks. I have a special interest in developing online resources for sustainable and efficient storage of biological information See my projects page for more about my research focus.
Computational Biology/Bioinformatics, Genomics, Proteomics, Proteogenomics, Cancer Genomics, Network Biology , Systems Biology


SHADOW OF A DOUBT A film review by Mark R. Leeper Copyright 1991 Mark R. Leeper
Capsule review: The Hallmark Hall of Fame again does a very creditable remake of a classic story. This time they have taken Hitchcock's suspense film SHADOW OF A DOUBT and nearly everything works. Rating: +2 (-4 to +4).
When I was growing up, I was aware that there were a series of dramas brought to television by the Hallmark Hall of Fame. It seemed that near card-sending holidays Hallmark would do a television play, and some were decent, but I would have preferred to go out to a movie any day. I think as I got older Hallmark really did get better. I know my perspective changed, but I genuinely feel that objectively they improved also. I first noticed how good they had gotten in 1979 with the remake of ALL QUIET ON THE WESTERN FRONT. I liked the film a lot and really wanted to see the original classic film. Actually I was a bit disappointed by the original, which did not seem to be so detailed a story or have so well-developed characters. But I told myself it was not a fair comparison. The original was an early sound film before a lot of techniques were developed. Besides, I'd seen the remake first and it had formed my opinions on the story. I also preferred their BEAUTY AND THE BEAST to Cocteau's, but then I am not a big fan of Cocteau's style.
I ran out of excuses with Michael Tuchner's HUNCHBACK OF NOTRE DAME. I had seen the three major film productions with Lon Chaney, Charles Laughton, and Anthony Quinn in the title role. Anthony Hopkins was the best Quasimodo and this production was the most detailed. I have also liked some of their original productions, but have only recently started paying closer attention to them. DECORATION DAY was among the best five films I saw last year. If I was going to put made-for-television movies on my "Top Ten of the Year" list, this would have made the list. Now Hallmark is back, remaking the classic film. This may be their first real suspense film, but they are starting with grand style.
Overall, the production values of SHADOW OF A DOUBT were very good. They used the original script, which was co-authored by Thorton Wilder. The murderous gigolo "Uncle Charley" was played by Mark Harmon, in the role that Joseph Cotten originally filled. Now, Harmon is a lot better looking than Cotten--almost pretty. Had someone with Harmon's looks played the role in the Hitchcock, you have never have been able to put someone with Cotten's looks in the remake. But putting someone better-looking in the role of the charming and villainous lady-killer works just fine. Harmon is not a very good actor and he does not project much emotion. In this role his synthetic charm and the veiled emotion work in his favor. The remake on its own is a pretty good suspense film.
The Hitchcock film was made in 1943 and, being made during the war, it was intended to show the soldiers what they were fighting for. It was a portrait of somebody's idea of an ideal American town: pearly-white, Anglo- Saxon, Protestant. It was a town where just about everyone sees each other in church. It was set in its present of 1943. The remake is a slight update, being set in 1953, the year Fidel Castro was captured and imprisoned--we hear about the event on the console radio. That really is the film's one questionable note. In a town like Petaluma, California, I doubt there were still many wind-up Victrolas in 1953. With that one minor objection, this remake is done in fine style. I would give it a +2 on the -4 to +4 scale.
Mark R. Leeper att!mtgzy!leeper<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Homepage - The Sun
Top Bar
Main navigation
Our beers
Contact
Customize your label
See our recipes
Our beers
The Noon
The Sunset
The Eclipse


Now more secure, smarter and easier to use—helping you save time
and do more with your inbox.
The ease & simplicity of Gmail, wherever you are.
See what’s new at a glance and decide what you want to read and respond to.
Get nudges that remind you to follow up and respond to messages, so that nothing slips through the cracks.
View attachments, RSVP to events, snooze messages and more without opening any emails.
Gmail blocks 99.9% of dangerous emails before they reach you. If we think something seems phish-y, you’ll get a warning.


| ls.size {mgcv} | R Documentation |
Produces a named array giving the size, in bytes, of the elements of a list.
ls.size(x)
x |
A list. |
A numeric vector giving the size in bytes of each element of the list x
. The elements of the array have the
same names as the elements of the list. If x
is not a list then its size in bytes is returned, un-named.


--- layout: post title: 'Scrum doesn''t do anything' date: 2008-05-27 00:00:00 category: software-development ---
I found this fantastic post from Tobias Meyer on the scrumdevelopment mailing list:
"There has been a lot of discussion on this list recently about what Scrum can and can't do, what it does or doesn't do, and how Lean can do things Scrum can't do, and so on. It is starting to feel like meaningless noise.Scrum doesn't do anything. Nor does Lean. People do things.
In the end it doesn't matter what names you use for your processes, good people will do good work and continuously improve what they do. So much of the discussion around Lean versus Scrum (etc.) is about marketing hype, selling consulting and training services, and cornering the market with new name-brands. It is easy to confuse people new to this list about the nature of Scrum.
Scrum is not a methodology, it is not a process. It is a simple framework underpinned by some common sense principles. Scrum offers individuals and organizations the opportunity to continuously improve the way they work. It provides a space for people to behave like human beings, with trust, respect and passion. That's about it. But that is huge.
 Nor will Lean. Nor will XP. Nor will any new brand-name Agile "solution". So get over it -- and more importantly, get on with it. There is work to be done :]"


My name is Dillam Diaz. Prior to joining the M.Sc. Computing Science program at the University of Alberta, I completed my bachelor’s degree in Biomedical Engineering at the University of Rosario and Escuela Colombiana de Ingenieria Julio Garavito. During my undergraduate studies in Colombia, I also worked as an Automation Engineer in the steel industry, developing hardware and applying autoencoder systems.
October 2018
July 2018
March 2018
November 2017
October 2017
International student pursuing a master’s degree in Computing Science
Student research working on emotion detection and activity detection with deep neural networks based on head movements and biosignals(EEG, EOG, EMG)
Passionate about developing technology to improve the lives of older adults
Python, Tensorflow, Panda
ROS, Microframework
Unity, Blender
Matlab
Java,C#,PHP
SQL, JSON, HTML
Internship Machine Learning Evaluation for connected services
Research and Teaching Assistant, Department of Computing Science
Operations Manager


--- layout: default title: SNSM'17 - Scientific Committee meta: The scientific committee members of SNSM keyword: computer science conference, multimedia conference, human computer interaction conference, information technology conference, information system conference, ergonomics conference, human factors conference, cybernetics conference, computer science, multimedia, human computer interaction, information technology, information system, ergonomics, human factors, cybernetics ---
The Scientific Committee list is currently being updated. We appreciate your patience!


Students in "Creative Coding" manipulate webcam pixel data to create algorithmic reflections of our analog world. Each student creates a particular "effect" can be viewed by clicking a button below.
Press the change size button to change the size and number of the screens.


Man closest to D.B. Cooper skyjacking mystery shares his story
Man sat near Cooper during leg of flight
SEATTLE - - After 48 years of not talking with the Seattle news media, the man closest to the D. B. Cooper skyjacking mystery is finally breaking his silence.


--script src="../../browser/reasondb.js">
Basic Example: Open debugging tools to view activity. The example populates localStorage in Chrome and Firefox. Review localStorage using browser debugging tools to see database structure after running tests.


- Nuget Package
- {{nugetPackageName}}
- Documentation
- View
- Commands are a request for the execution of an action.
- Commands update the state of resources managed by an application service.
- Unlike domain events, commands can have an associated result returned
by the command handler.
-
When a command is handled by subscriber, it is common for a domain-event to
be published notifying other application components of the state change.
-
Command handler methods can be synchronous or asynchronous based on the code it
needs to call. However, from the perspective of the code sending the command,
the result is always asynchronous. This decouples the caller from the handler
and allows the handling code to change overtime without having to refactor any
of the calling code.
-
The base implementation invokes command subscribers that are declared in-process to the
code invoking the command. Other plugins, such as RabbitMQ and AMQP extend this base
pipeline allowing commands to be sent to queues and handled by out-of-process subscribers.
- A command can have only one in-process subscriber.
-
Since other plugins can extend the base messaging pipeline, sending out-of-process
commands are exactly the same as sending in-process commands. The code sending the
command is completely decoupled from how the command is delivered.
-
Likewise, the subscribing to commands is the almost identical regardless of being handled
in-process or out-of-process. The only difference are attributes specified on command handler
methods defined by the plug-ins extending the pipeline. The attributes are used to specify,
for example, the queue to which the command handler should be bound. Also, the plug-ins extending
the base pipeline usually provide a way of mapping a given command type to to an external messaging
concept, such as a queue, to which it is delivered when sent.
- Commands are defined by implementing the ICommand interface.
The base abstract Command class provides a default implementation
from which commands can be derived.
- It is best for commands to be simple POCOS that are immutable.
- Commands are often created from a REST Api request by the controller.
- Commands are sent by calling the Send method on the IMessagingService
service contract.
-
Commands are handled by component's implementing the IMessageConsumer
marker interface with a method accepting a parameter of the command type to be handled.
The method must also be decorated with the InProcessHandler attribute.
The handler method can also have a CancellationToken passed as the second parameter.
This example defines a command containing an array of integers from which the handler calculates the range.
The WebApi allows two sets of integers to be passed. The controller transforms the posted model into two
commands - one for each set of values. The controller sends each command at the same time and awaits their
completion. To involve an asynchronous call, the handler delays for 5 seconds. If either array of integers
contains zero elements, an exception is thrown for the range calculation.
- The CalculateRange class derives from the base Command class.
- The CalculateRange class specifies Range as it result class.
- The CommandController sends the command using the IMessagingService contract.
- The RangeHandler class shows an example of the command being handled.


The Bootcamp Stigma
January 22, 2016
I have been coding for: 4 weeks
Ah, what a rosy picture of coding bootcamps:
I’m sure many of us were seduced by the picture coding bootcamps often paint: leave a job you’re not passionate about, give up a few months of your life, and come out with a much higher-paying, more satisfying job than the one you left. Poof! Good deal, right? Advertisements, newspaper articles, and anecdotes from friends often point to this version of the story.
Woah look at all those companies that are going to be fighting to hire me!
Unfortunately, this makes it sound like a pretty effortless path. Cough up some money for a sweet dev gig. I can imagine I’d be quite offended if I dedicated my entire life to developing a certain skill and then someone came along claiming to teach that particular skill in weeks.
We all know by now that this is a simplified and ignorant view of what bootcamps are actually like. I don’t think any of us expect to be handed a job just because we paid money for this program. However, we’ll all be more successful in the industry if we get ahead of the negative stigma and acknowledge it head on. Let’s start with how coding bootcamps came about.
The History
Software development is a rapidly growing industry, with an estimated 17% growth over the next 10 years. At least 50 coding bootcamps have sprung up across the country in the past 5 years as an answer to this problem of increasing demand. New programming languages are popping up and older languages are evolving - it’s tough to keep up! The type of guided learning environment bootcamps provide often give students the hands-on experience traditional computer science degrees often don’t.
What do people in the industry say?
- The “STEM crisis” isn’t real. There are plenty of skilled workers to fill jobs in technical fields.
- There is a glut of programmers.
- Higher education is elitist and overvalued.
- Coding bootcamps help you get to the core of skills you’ll use every day in your career - there’s no “fluff".
- There’s no way you can squeeze a 4-year degree into 3 months.
- The tech sector is a meritocracy - degrees don’t matter.
- DBC students learn more in 9 weeks than some devs learn in the first few years of their professional careers.
- Bootcamps churn out lower quality workers, while maximizing their own profits.
- Bootcamp grads are often more up-to-date on the latest coding methodologies and tools than experienced developers.
The solution?
Know these conflicting perceptions. In many cases, we'll be competing with computer science grads for jobs they’d traditionally get. This is a tough pill to swallow for those that have taken that route. Stay humble. You will likely have the least technical experience of anyone at your company. Proactively acknowledge and applaud the strengths of your teammates, admit your short-comings, and seek out guidance from those who are more experienced. Find a mentor and emphasize your willingness to keep learning. It is our responsibility to show that bootcamp grads have the skills necessary to keep up - our performance will continue to shape these perceptions! Keep in mind that DevBootcamp is an incredible kickstart to our careers, but the journey has just begun.


Backpropagation Demystified
Backpropagation is a well-established approach to train a neural network. The main goal of training a neural network is to determine the optimum values of connection weights (parameters) of the neural network. Following equation is used to update the connection weights
where, $w_i$ is the connection weight and $\alpha$ is the learning rate. Learning rate $\alpha$ controls magnitude of the change in the connection weight on each step of training process.
In the above equation, the partial derivative of the $E(w_i)$ w.r.t $w_i$ gives the direction in which the weight should be modified to reduce the error. In the equation, the only computationally expensive operation is to compute partial derivative. Without using backpropagation approach, with the increase in the number of layers of neural network computional complexity of the partial derivative increase exponentially. Using backprogation technique, the partial derivative of error w.r.t connection weight can be computed efficienly.
Refresher: Intuitive meaning of derivative and partial derivative
Let’s assume is time allocated by a person for the exercise and is the weight of the person. The derivative of w.r.t. means if the person makes a small change in the time allocated for the exercise how much the weight will change. Mathematically, the derivative of w.r.t. is represented as .
We know that diet () also has an effect on the weight of the person. Analysis of change in the weight of the person w.r.t. a small change in the time allocated for the exercise keeping diet same is given by the partial derivative of w.r.t. . Mathematically, the partial derivative of w.r.t. is represented as . Similarly, if the person keeps the exercise time the same and makes a small change in the diet it is represented as the partial derivative of w.r.t. which is represented as .
Figure 1: Toy neural network
Figure 1 shows the simplest possible neural network which is good enough to understand details of the backpropagation algorithm. In the figure, input-layer is layer-1 (), hidden-layer is () and output-layer is (). and are input to the neural network, blue nodes are nodes of hidden-layer and the red node is output-node. There are 6 connection weights to . In the training process we want to determine values of the connection weights which minimize error. In this post regression error is minimized to explain the backpropagation technique but the idea remains the same for the classification problem as well.
Square Error (E) =
As constant does not affect optimization problem, square of error is divided by 2 just to make the computation clean.
In Figure 1, each node (red/blue) represents three computations namely, multiplication of an input and a weight, sum of all the results of multiplications and finally activation score which is non-linearity of the summation. Figure 2 shows the clear and detailed view of the neural network which consists of multiplier, summation unit and non-linearity unit. Combination of the multiplier, summation and non-linearity function in Figure 2 is equivalent to a node in Figure 1.
Figure 2: Equivalent detailed view of the toy neural network
is z of node-1 in layer-2 (Note: subscript => item number and superscript => layer-number) and is the activation score of node-1 in layer-2. Following equation is used to compute and in the figure 2
where, f() is a non-linearity function. Usually, non-linear function f() are sigmoid, tanh or Relu.
Backpropagation is all about computing partital derivative efficiently. Using backpropagation, we can calculate effectively where i = {1, 2, 3, 4, 5, 6}.
The partial derivative of error with respect to weight gives amount by which error will change if there is a small change in the weight. To calcualate the partial derivative of error w.r.t. weight chain rule is used.
To come up with the expression from Figure 1 can be confusing. However, if we construct a detailed view of the neural network as in Figure 2 then getting the expression is simply following chain rule from output to input (note it is backward). For to , the partial derivatives of error with respect to the weights can be calculated in the same way.
OMG!! expression of such a simple neural network is so complex. There will be total six partial derivatives, so how can we compute them effectively? The answer is dynamic programming. Dynamic programming is the fancy name of ** memorization and reutilization of computation.** We use the chain rule to get the expression of the partial derivative of error w.r.t. weights. To compute such complex expressions efficiently dynamic programming is used.
Layer-3
For ease of representation we represent as . Note that and have same sub-script (item-number) and superscript (layer-number).
To evaluate , the computation of can be utilized (memorized and reutilized) hence the repeated computation is avoided.
First chain rule is used to obtain the expression and then computaiton of is utilzed to compute . Following the similar steps, can be computed.
### Layer-2 The computations of layer-3 is reused for the computation of layer 2. Note that, computation starts from output toward input using the chain rule. The reutilization of the computation can be seen as message passing in the backward direction.
Similarly,
It is observed that term which appears in the computation of will be memorized and reutilized in the computation of .
Let’s calculate the partial derivative of error with respect to weight.
let’s say and Then,
We start with following expression, which looks like there is a lot of computation. But, utilizing the dynamic programming in each layer computation is minimized. Now, which looks much simpler and also computationally very efficient.
Similarly, the expression for the partial derivative of error w.r.t. remaining weights can be computed. We observed that if we break down a neural network into a detailed diagram as in Figure 2 then it would be easier to come up with chain rule. Once we have the expression, the long and complex expression can be computed efficiently using dynamic programming.


Need Audio/Visual services to go along with your live event? Kingston Media offers high-definition projectors, large event screens, lighting, sound and other elements to make a real impact at your live event.
Need Audio/Visual services to go along with your live event? Kingston Media offers high-definition projectors, large event screens, lighting, sound and other elements to make a real impact at your live event.
Audio/Visual Equipment


Sacred Tree Antiques & Collectibles
About
Gallery
Drive to Vale, Oregon and enjoy small town prices on quality antiques and collectibles.
Visit Us
293 Washington St. Vale, Oregon
208-559-2786


Rohit Sharma's Breathtaking Catch During Pink Test Has Become a Hit Meme | POSTS
Sharma's brilliant catch brought an end to Bangladesh captain Mominul Haque's short stay.
India opener Rohit Sharma took a breathtaking one-handed catch at second slip on day one of the ongoing Pink-Ball Test in Eden Gardens, Kolkata on Friday. Sharma’s catch brought an end to Bangladesh captain Mominul Haque’s short stay.
Umesh Yadav was the bowler, who bowled the ball at a good length to the left-hander and it tailed in slightly. Haque tried to defend it, but it found the outside edge. Rohit at second slip had to dive across to pull off the catch in front of the first slip where India captain Virat Kohli was present.
Once the video of the catch surfaced on Twitter, fans got creative and came up with hilarious memes:
Meanwhile, Captain Virat Kohli remained unbeaten on 59 while his deputy Ajinkya Rahane was on a confident 23 as India made 174/3, ahead by 68 runs at stumps on the opening day of their maiden Pink Ball Test – the second Test against Bangladesh at the Eden Gardens in Kolkata on Friday.
Brief scores: India 174/3 (Virat Kohli 59*, Cheteshwar Pujara 55; Ebadat Hossain 2/61) lead Bangladesh 106 all out (Shadman Islam 29; Ishant Sharma 5/22, Umesh Yadav 3/29, Mohammed Shami 2/36) by 68 runs
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on Cricket Latest News on India.com.
Story Timeline
- 23 Nov 2019 Rohit Sharma's Breathtaking Catch During Pink Test Has Become a Hit Meme | POSTS
- 23 Nov 2019 WATCH: Bangladesh Pacer Lauding Kohli's Signature Cover-Drive is Unmissable
- 23 Nov 2019 Pant Released to Play Mushtaq Ali, KS Bharat to Join as Saha's Cover
- 22 Nov 2019 It s Past My Bedtime Bangladesh Coach Feels Day-Night Tests Not Exciting as Day Tests
Comments - Join the Discussion


Fifth Week of RC
Day 4: Chrome Extensions
March 21, 2019
As previously mentioned, I'm working on a project called EmoReply with a fellow Recurser, Jamie. Though the project originated as a parody of Gmail's autoreply feature, we decided to make it a chrome extension, so that it can provide angsty autoresponses across various platforms. Whether you're writing an email, commenting on a NYTimes article, or posting on a forum, EmoReply is at your service.
Jamie and I decided to make a roughly-functioning chrome extension before integrating the machine learning component. Our goal was to build a chrome extension with a text box, which would take user input, then display the input at the extension's bottom. Once we have our angsty auto reply learning, we'll display the emoreplies at the extension's bottom.
We learned various things while working on the chrome extension. For example, the developer tools' console for a chrome extension is different than the developer tools' console for the main page. Additionally, if you go to `chrome://extensions`, you can see any errors that occur for each extension. However, the displayed errors won't automatically go away after you fix them. Instead, you'll need to hit "clear errors." (This seems like questionable UX to me.)
Struggling with a bug, Jamie and I posted a question on Recurse's internal chat, Zulip. We were lucky to get a response from another Recurser, Cory, who teaches a class on chrome extensions at a local college. If this doesn't illustrate the richness of Recurse's intellectual community, I don't know what does. Cory agreed to meet with me, and with his help, I was able to make the extension display text in 15 minutes. Yay, collaboration!
When Jamie and I looked through chrome extension tutorials, we saw extensions that had two scripts, popup.js
and background.js
. We made our extension with only one script, which we called background.js
. At a certain point, we had mutually acknowledged that this was a misnomer, because we were using the background.js
script to work on the popup. "We should probably call it popup.js," we said, then kept coding in the background.js script.
This was silly.
Cory pointed out that Jamie and I had imported the poorly-named background.js
script into popup.html
with the below code:
We had also made reference to the background.js
script in the manifest.json
, as seen below:
"background": {
"scripts": ["background.js"],
"persistent": false
},
Since we made reference to the same script twice, we were attempting to run it simultaneously in two different places. This, too, was silly.
As a quick fix, I renamed our existing background.js
file as popup.js
and made reference to it in the popup.html
, like so.
Then, as a quick fix, I created a blank background.js
and kept the reference to it in manifest.json
.
The chrome extension now displays the input text after the button is clicked. Huzzah! Here's the repo, for anyone who is interested in more details. EmoReply


Login
Sign in
Login
Sign in
Menu
SAGE test
Proceed with the test. Take your time. Whatever, I don't care.
Have you had any problems with memory or thinking?
Yes
Only occasionaly
No
Have you had any blood relatives that have had problems with memory or thinking?
Yes
No
Have you ever had a major stroke?
Yes
No
A minor or mini-stroke?
Yes
No
Do you currently feel sad or depressed?
Yes
Only occasionaly
No
Name the following pictures:
Write your answer here:
Write your answer here:
priority_high
phone
Call
people
Team
info_outline
Info


Select a button by choosing your row, then the column, then indicating correct button (enter) by double clicking. To go back a step (from row selection to column selection, for example), triple click any key.
Hold any keyboard key to scroll to your row, then your column by holding the key.
Double press for "enter" - double press any keyboard key twice indicate that you'd like to make that selection.
If you made the wrong column or row selectio, triple click to indicate so
Test results
Anything else you have tried out
I misplaced my finished files (likely on another computer) so I'm uploading what little is on this one. I will upload when I can, though I'm not sure it can be accepted?
About this app
Include information here about your app.
I misplaced my finished files (likely on another computer) so I'm uploading what little is on this one. I will upload when I can, though I'm not sure it can be accepted?


---
layout: page
title: Building Stacks
description: The following is a guide to building and running Stacks locally. You’ll need to be able to build Stacks to contribute to our documentation or add new classes to our CSS library.
---
{% header h2 | Install a full Ruby development environment %}
{% header h3 | macOS %}
You’ll need a full Ruby development environment in order to run our static site generator. Let’s start by opening our Terminal app and installing a few things.
- Install Xcode. You can download it directly, or install via the Mac App Store.
- Install Xcode’s command line tools
xcode-select --install
- Accept the Xcode license.
sudo xcodebuild -license accept
- To Install a fresh version of Ruby, we’ll use Homebrew. Install it by running
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
- Now you can use Homebrew to install Ruby
brew install ruby
- You’ll need to add
export PATH=/usr/local/opt/ruby/bin:$PATH
to your .bash_profile
in your user directory. If a .bash_profile
, create one and add that line to the file.
- Now’s a good time to quit your Terminal and relaunch it. This will make sure your session is aware of the new dependencies we’ve installed. Don’t just close the window, quit the whole app.
{% header h3 | Windows %}
Download and install a Ruby+Devkit version from RubyInstaller Downloads. You’ll want to restart your computer before moving on.
{% header h2 | Clone the repo %}
There are two common ways to clone a repo:
- Use the command line:
git clone https://github.com/StackExchange/Stacks.git
- Use GitHub’s desktop app.
- Download and install GitHub Desktop.
- Login with your GitHub credentials.
- Clone the Stacks repo.
{% header h2 | Get Node and NPM installed %}
We use a bunch of NPM dependencies to process and package up Stacks for delivery. You’ll need to install them.
- Install Node & NPM
- Open the Stacks repo in a Terminal window.
- Install the NPM dependencies.
npm install
{% header h2 | Install the documentation depenencies %}
We use Jekyll to generate our site. We use Bundler to install Jekyll and its dependencies.
- All the documentation dependencies can be automatically installed all at once by using our handy install command:
npm run init-docs
{% header h2 | Running Stacks %}
That should do it for all our dependencies. You’re now able to run Stacks. {% icon Tada | native va-text-bottom %}
- From the top level of the Stacks repo, run our main script
npm start
- Visit your local copy of Stacks at
http://<IP_ADDRESS>:4000/
{% header h2 | Getting Help %}
Installing dependencies can be frustrating, and we’re here to help. If you’re stuck, the Stacks team is always available in #stacks. If that doesn’t work, try opening an issue.


Syntax:
gensym &optional x => new-symbol
Arguments and Values:
x---a string or a non-negative integer. Complicated defaulting behavior; see below.
new-symbol---a fresh, uninterned symbol.
Description:
Creates and returns a fresh, uninterned symbol, as if by calling make-symbol. (The only difference between gensym and make-symbol is in how the new-symbol's name is determined.)
The name of the new-symbol is the concatenation of a prefix, which defaults to "G", and a suffix, which is the decimal representation of a number that defaults to the value of *gensym-counter*.
If x is supplied, and is a string, then that string is used as a prefix instead of "G" for this call to gensym only.
If x is supplied, and is an integer, then that integer, instead of the value of *gensym-counter*, is used as the suffix for this call to gensym only.
If and only if no explicit suffix is supplied, *gensym-counter* is incremented after it is used.
Examples:
(setq sym1 (gensym)) => #:G3142 (symbol-package sym1) => NIL (setq sym2 (gensym 100)) => #:G100 (setq sym3 (gensym 100)) => #:G100 (eq sym2 sym3) => false (find-symbol "G100") => NIL, NIL (gensym "T") => #:T3143 (gensym) => #:G3144
Side Effects:
Might increment *gensym-counter*.
Affected By:
Exceptional Situations:
Should signal an error of type type-error if x is not a string or a non-negative integer.
See Also:
Notes:
The ability to pass a numeric argument to gensym has been deprecated; explicitly binding *gensym-counter* is now stylistically preferred. (The somewhat baroque conventions for the optional argument are historical in nature, and supported primarily for compatibility with older dialects of Lisp. In modern code, it is recommended that the only kind of argument used be a string prefix. In general, though, to obtain more flexible control of the new-symbol's name, consider using make-symbol instead.)


Which programming language should I study??
Click here to take the Epicodus survey
what's your name?
What is your favorite day of the week?
Favorite day
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
Art or the Sciences?
🎨 or 🔬
Art
Science
What's your favorite meal of the day?
Favorite meal
Breakfast
Brunch
Lunch
Dinner
Dessert
What's your favorite continent?
Your fav continent
Africa
Asia
Europe
Australia
N. America
S. America
Antarctica
Who's your favorite Portland TrailBlazer?
Favorite player
Blaze the Trail Cat 🐱
Chief
Anyone but Meyers Leonard
Dame
The other Steph Curry
Meyers Leonard
Click here to submit
Click here to remove survey
Dear
,
According to our advanced comparator algorithm (powered by ECMASCript ™), the approrpriate course for you is
. We hope that you enjoy the course and work hard to become a great programmer!


Steam Store
下午 2:58您愿望单中的一件物品正在促销!
to be or not to be, that is a question...
您愿望单中的一件物品正在促销!
to be or not to be, that is a question...
您愿望单中的一件物品正在促销!
to be or not to be, that is a question...
您愿望单中的一件物品正在促销!
to be or not to be, that is a question...
您愿望单中的一件物品正在促销!
to be or not to be, that is a question...
您愿望单中的一件物品正在促销!
to be or not to be, that is a question...
您愿望单中的一件物品正在促销!
to be or not to be, that is a question...


Rental Agreement Information
- A $50 security deposit is to confirm reservations.
- A valid drivers license is in hand at time of rental.
- No one under 12 years of age are allowed to ride on the scooters.
- Fuel charge not included.
- Unlimited mileage.
- Liability insurance is free of charge.
- Reservations rate requires 24 hour prior notification.
- Free delivery to Cozumel hotel, Airbnb, or private residence for 2+ day rentals.
- Payment via Google Pay, cash, credit card, debit card, or PayPal.
- Rentals may be returned to either location.