11institutetext: Research Group on Intelligent Engineering and Computing for
Advanced Innovation and Development (GECAD), Porto School of Engineering
(ISEP), 4200-072 Porto, Portugal
11email<EMAIL_ADDRESS>
# Deep Learning for Short-term Instant Energy Consumption Forecasting in the
Manufacturing Sector

Nuno Oliveira  0000-0002-5030-7751 Norberto Sousa  0000-0003-2919-4817 Isabel
Praça  0000-0002-2519-9859

###### Abstract

Electricity is a volatile power source that requires great planning and
resource management for both short and long term. More specifically, in the
short-term, accurate instant energy consumption forecasting contributes
greatly to improve the efficiency of buildings, opening new avenues for the
adoption of renewable energy. In that regard, data-driven approaches, namely
the ones based on machine learning, are begin to be preferred over more
traditional ones since they provide not only more simplified ways of
deployment but also state of the art results. In that sense, this work applies
and compares the performance of several deep learning algorithms, LSTM, CNN,
mixed CNN-LSTM and TCN, in a real testbed within the manufacturing sector. The
experimental results suggest that the TCN is the most reliable method for
predicting instant energy consumption in the short-term.

###### Keywords:

Energy Forecasting Temporal Convolutional Network Long-Short Term Memory Deep
Learning Time series.

##  1 Introduction

Electricity powers our current world, from illumination and home appliances to
cars and industrial machines. Energy consumption is tied to the most varied
factors, such as time of year and weather, and due to fundamental issues in
storing that much power [1], energy production needs to ramp up and down in
order to deal with times of high usage while not wasting resources in the
downtimes.

This planning and resource management needs to happen, both in the short-term
where predictions are made for minutes, hours, weeks and months, and in the
long-term where years and decades are provisioned for. Due to the differences
involved in both these time ranges and the strategies used in each one, the
energy forecasting domain can be divided in two subtopics: short-term and
long-term. In the short-term, the focus is on predicting energy consumption
values, to aid in planning for energy production and grid maintenance. Whereas
in the long-term, planning for grid expansions and future investments are the
objective, even influencing the economic development of a given geographic
zone [2].

Although both types of energy forecasting have high importance and impact in
everyone’s life, the scale of long-term forecasting surpasses most commercial
and industrial entities. On the other hand, short-term forecasting can be
utilized to improve efficiency of buildings [3] and plan around cheaper
renewable energy utilization [4], information usable in small scales in the
most varied industries.

In short-term energy forecasting, several probabilistic [5] and regression [6]
models have been deployed for many years as state of the art approaches to
this problem. Now, with the advent of Machine Learning (ML), new techniques
are being developed, taking advantage of advances in computing power and
Artificial Intelligence (AI) research to improve the old methods and develop
new ones.

Older, more involved physical models, or white-box models, require detailed
building information, such as heating, mass and energy consumption, as well as
a high amount of manual tuning. When in the initial stages of design or
development of a given building, such parameters might not be available.
Despite the high setup effort, models of this kind offer a high degree of
explainability for their predicted values. Contrarily, data-driven models, or
black-box, where ML models are trained using historic consumption data and
basic building information to compute future consumption [7], offer simplicity
and straightforwardness in deployment in conjunction with state of the art
results, with the caveat that the relation between the input data and output
results is not mappable.

In the context of Cyberfactory#1 [8], an holistic approach to a Factory of the
Future, originated energy consumption data from a real life example from the
manufacturing industry, namely fabric manufacturing. Therefore, this work
proposes a comparison of 4 Deep Learning (DL) models, in a data-driven
approach to the energy forecasting domain, applied to a current and relevant
dataset.

This work is organized into multiple sections that can be described as
follows. Section 2 provides an overview on current state of the art of the
energy forecasting domain. Section 3 describes the main characteristics of the
used data, as well as of the explored models. Section 4 presents the obtained
results performs a comparison between the different models. Finally, Section 5
provides a summary of the main conclusions to be drawn from this work,
appointing further research lines.

##  2 Related Work

The employment of ML is not a new endeavor in historical energy forecasting,
with several works exploring interesting approaches to this domain, starting
with simple model implementations, such as Random Forest (RF) in [9] to more
sophisticated multi-model ensembles [10].

However, neural networks dominate the literature, with several different
implementations being proposed, such as in [11], where Che-Jung Chang et al.
demonstrated a backpropagation neural network, achieving promising results for
country-wide eletricity consumption and [12] where X.J. Luo et al. developed a
feedforward neural network for load prediction in the context of temperature
regulation in buildings.

A very popular approach sees the use of Long-Short Term Memory (LSTM) model
architectures with Convolutional Neural Network (CNN) layers [13, 14, 15, 16,
17, 18]. LSTMs are able to model temporal data and generate predictions from
time series, and CNNs are able to extract interesting features from time
series predictions. For this reason, this ML pipeline is very utilized in the
literature with some of the works improving on it with interesting results.

In [15], X.J. Luo et al., employed a genetic algorithm to optimize the
architecture of a LSTM, managing to improve over other hyperparameter tuning
approaches for the same model in the same dataset.

In [14], Nivethitha Somu et al., introduces a cluster analysis step to the
usual CNN-LSTM pipeline, using k-means to understand energy consumption trend
by clustering time series data into consistent individual groups.

In [16], Ashutosh Kumar Dubey et al., compared 3 models in forecasting time
series data, LSTM, AutoRegressive Integrated Moving Average (ARIMA) and its
seasonality aware implementation, Seasonal ARIMA (SARIMA). The authors
demonstrate empirically the superiority of the LSTM implementation for the
chosen dataset.

Temporal Convolutional Networks (TCN) also produce interesting results due to
the time series based nature of the used data. Examples of this can be seen in
[19], where a TCN is used in conjunction with a Light Gradient Boosting
Machine (LightGBM) to forecast short-term load for several industries in the
context of different countries. Furthermore, in [20], H. Wang et al., deploys
a TCN on a hourly consumption dataset to great success, surpassing even the
reliable LSTM model.

In long-term load forecasting, the involvement of several external
socioeconomic factors impacts the predictions made by the usual models. This,
allied to the great importance of the results to future developments and
investments in a given geographical area, requires more sophisticated
approaches.

In [21], T.Q.D. Khoa et al., factors such as gross state product, consumers
price index and electricity tariff are used as input multiple neural network
based models. The historical data used encompasses 20 years of readings,
achieving good results with the Wavelet Network model. On the other hand, in
[22] Gamze Nalcaci et al., expands the normal approach for short-term
forecasting, stretching the predictions for years in the future. The data used
contains readings of historical energy consumption from the Turkish
electricity distribution network, in conjunction with weather data, and allows
not only long-term but also short-term forecasting. Three models were
employed, Multivariate Adaptive Regression Splines (MARS), Artificial Neural
Network (ANN) and Linear Regression (LR), with MARS achieving more stable
results.

##  3 Materials and Methods

In order to determine and compare the short-term energy forecasting
capabilities of ML algorithms, four types of ANNs were selected, applied and
compared using a real testbed under the manufacturing context. Hence, in this
section the main characteristics of the dataset which served as case study are
presented along with the main properties of the selected algorithms.
Additionally, the employed test design is also discussed in detail, addressing
how hyperparameter tuning was performed, how different algorithms were
compared and which evaluation metrics were considered. The objectives of this
study can be described as follows:

  * •

Compare the short-term energy forecasting performance of four ANNs: LSTM; CNN;
mixed CNN-LSTM network; and TCN;

  * •

Understand the effect of different context windows (an hour, a day and a week)
in both algorithm’s predictive performance and computational cost;

  * •

Conclude which technique is best suited to be used in the context of the
Cyberfactory#1 research project, a fully integrated, real-world, energy
forecasting setting.

###  3.1 Dataset

In the context of the Cyberfactory#1 research project, data from 10 energy
consumption analysers, expressed in Watts and scattered along a given fabric
factory’s shop floor, was collected for a period of approximately two years,
from the 1st of January, 2020, to the 1st of December, 2021. Energy
consumption data was recorded every 4:30 minutes, with small variations in
seconds, totalling about 2,200,000 records (220,000 records per each
analyser). Table 1 presents the monitoring resource of each energy analyser:

Table 1: Energy consumption analysers. Analyser | Resource
---|---
$1^{st}$ | General QGBT
$2^{nd}$ | Compressor
$3^{rd}$ | Jacquard Loom
$4^{th}$ | Cad Design
$5^{th}$ | Screen/offset painting
$6^{th}$ | Rapier Loom
$7^{th}$ | Cutting Division
$8^{th}$ | Dyeing Machine 1
$9^{th}$ | Dyeing Machine 2
$10^{th}$ | Dyeing Machine 3

For this study only data from analysers 6 and 7 was considered in order to
reduce significantly the amount of required computational resources to train
and optimize the hyperparameters of the selected models. These analysers were
selected due to their relevance in the domain, as the cutting division and
rapier loom can be seen as core for the factory operation.

###  3.2 Cleaning

Before splitting the dataset and applying further processing operations, an
initially cleaning step was performed in order to guarantee data consistency.
This step was necessary since the raw measures collected from the energy
analysers over time were susceptible to arbitrary errors in sensor readings
and communication failures, resulting in anomalous instant energy consumption
records such as unrealistically large values or negative ones. Furthermore,
communication failures/system failures led to periods of time were no data was
captured, resulting in gaps much larger than the expected average of 4:30
minutes between readings. This lack of equally-spaced measures can be
troublesome for algorithm’s predictive performance when framing the problem as
an univariate time series.

In order to clean the dataset and assure data consistency, three operations
were applied to the whole dataset:

  1. 1.

Cutoff Filter: This operation was firstly applied to every record in order to
remove crystal clear outliers from the dataset. The instant energy consumption
measures can not assume negative values, and similarly, values above a certain
threshold are so unrealistically large that can only be the product of an
erroneous sensor reading. Therefore, using the Cuttoff Filter across the whole
series, readings bellow a minimum threshold, $\alpha$, were replaced by the
value of $\alpha$, and values above a maximum threshold, $\beta$, were
replaced by the value of $\beta$. For the employed filter, $\alpha$ was set to
0 and $\beta$ to 10000.

  2. 2.

Window-based Outlier Substitution: This filter was applied to replace less
obvious outliers and produce a cleaner signal overall. It uses z-score outlier
detection, Equation 1, computed over a rolling window, $w$, of $n$ lag
measures in order to prevent look-ahead bias i.e, using future measures to
decide if the current one should be considered an outlier or not.

$z_{x}=\frac{x-\mu_{w_{x}}}{\sigma_{w_{x}}}$ (1)

where $z_{x}$ is the z-score of $x$, $\mu_{w_{x}}$ is the mean of all measures
of the window $w$ of $n$ lagged values prior to $x$ and $\sigma_{w_{x}}$ is
the standard deviation of that same $w$ window. For a given value of $x$, if
its z-score, $z_{x}$ is above a given threshold, $\omega$, $x$ is replaced by
$\mu_{w_{x}}$. This operation is performed for every element, starting at the
$n+1$ position of the series. For the employed filter, a rolling window of 7
days was applied and the threshold, $\omega$, was set to 3.

  3. 3.

Data Aggregation: To deal with the problem of equally-spaced measures, time-
based data aggregation was performed in the series as an additional cleaning
step. As the raw data was collected at an average rate of one measure every
4:30 minutes, the data aggregation must regard a time window, $t$, at least
equal to that same value. Hence, for an initial date, $d_{i}$, to an end date
,$d_{e}$, every instant energy consumption measure between the current date,
$d_{c}$, and the next date, $d_{n}=d_{c}+t$, was summed, equalling a new value
of aggregated energy consumption measures, $v$, that is indexed along with
$d_{n}$ as an entry of a resulting dataset. Then, $d_{c}$ takes the value of
$d_{n}$ and the aforementioned operation is repeated until $d_{c}$ becomes
greater or equal than $d_{e}$. For the employed operation, a time window, $t$,
of 5 minutes was selected. Additionally, the chosen dates for $d_{i}$ and
$d_{e}$ were 2020-01-01 00:10H and 2021-12-01 00:00H, respectively.

###  3.3 Test design

After applying every cleaning operation, the resultant dataset was split into
three different sets, training, validation and testing, with respect to series
continuity. The training set, comprised of 14 months, between 1st of January
2020 and 1st of March 2021, was used to train the four DL algorithms and to
provide a baseline for fitting preprocessing operations such as Min-Max
Normalization. Differently, the validation set, comprised of 3 months, between
1st of March 2021 and 1st of June 2021, was used as a testbed for
hyperparameter tuning. And, finally, the test set, corresponding to the last 6
months of the dataset, between 1st of June 2021 and 1st of December 2021, was
used to evaluate the predictive performance of the already tuned algorithms.

These experiments were repeated for different context windows, one day, one
hour and one week, corresponding to different versions of the dataset, with
the main purpose of understanding how the increase/decrease of lagged measures
affects both the predictive performance of algorithms and the involved
computational costs. Consequently, considering two analysers (6 and 7), four
DL algorithms (LSTM, CNN, CNN-LSTM and TCN) and three context windows (hour,
day, week), the training/validation/testing process was repeated 24 times with
an NVIDIA P4000 GPU with 8 Gigabytes of VRAM serving as hardware support.

###  3.4 Modeling

According to the review of the literature, four DL algorithms were selected to
be compared in the task of short-term instant energy consumption forecasting.
These can be briefly described as follows:

  * •

LSTM: An LSTM is a type of Recurrent Neural Network (RNN). Hence, it comprises
feedback connections that allow information to travel in a loop from layer to
layer, storing information about past computations through a hidden state
(network memory). LSTM cells were also designed to overcome the vanishing and
exploding gradient problem of typical RNN architectures, making them suitable
to learn long-term relationships between elements of the input sequence [23].

  * •

CNN: CNNs are very popular algorithms in image processing and object
recognition tasks. These were also adopted for time-series analysis due to
their receptive field capability, which allows them to explore efficiently the
time dependencies across several events of a given time frame [24].

  * •

CNN-LSTM: In a CNN-LSTM network, the receptive field capability of CNNs is
used to transform multiple inputs into high-level features that are then
processed by consequential LSTM layers. The CNN-LSTM network has shown
promising results in recent studies on energy consumption forecasting, in
particularly, for problems that regard multivariate time series [25].

  * •

TCN: The term TCN was first introduced to describe a generic architecture for
convolutional sequence prediction. It combines dilations and residual
connections with the causal convolutions needed for autoregressive prediction,
outperforming generic RNN architectures for a variety of tasks [26].

The input of each algorithm is three-dimensional with the shape of
($n_{samples}$, $n_{timesteps}$, $n_{features}$), where $n_{samples}$ is the
amount of energy consumption measures (14 months of 5 minute spaced measures
for the training set), $n_{timesteps}$ is the number of the considered time
window (12 for one hour, 288 for one day, and 2016 for one week) and where
$n_{features}$ is the amount of features for each time step. In this case
there is only one feature at each time step, the amount of instant energy
consumption, as the problem was framed as an univariate time series
forecasting task.

For each algorithm, several hyperparameters, such as the amount of the hidden
layers, learning rate and batch size were optimized using 10 iterations of
random search over a predefined grid of parameter values comprising about 500
possible combinations. For the LSTM, the employed hyperparameter grid is
presented in Table 2.

Table 2: Hyperparameter grid for the LSTM. Hyperparameter | Values
---|---
Dropout Rate | [0.1, 0.2, 0.3]
Nº of LSTM Layers | [2, 3, 4]
LSTM Layer Size | [64, 128, 256]
MLP Layer Size | [32, 64]
Learning Rate | [1e-4, 1e-3, 1e-2]
Batch Size | [32, 64, 256]

###  3.5 Evaluation

With respect to the literature, Mean Absolute Error (MAE) and Root Mean
Squared Error (RMSE) were selected as evaluation metrics. MAE provides clear
interpretation since it provides a direct overview of the average error, while
RMSE has the benefit of penalizing large errors since these are first squared
before being averaged. Both metrics are expressed as the same unit of
measurement as the target value, in this case, Watts [27, 28].

Both MAE and RMSE are presented in Equation 2.

$MAE=\frac{1}{n}\sum_{j=1}^{n}|y_{j}-\hat{y}_{j}|$ (2a)
$RMSE=\sqrt{\frac{1}{n}\sum_{j=1}^{n}(y_{j}-\hat{y}_{j})^{2}}$ (2b)

where $n$ is the total number of measures in the test set, $j$ is a given test
set measure, $y_{j}$ is the actual value of measure $j$ and $\hat{y}_{j}$ is
the predicted value for measure $j$.

##  4 Results and Discussion

The results presented in this section derive from the implementation of the
described methodology using Python programming language and it’s appropriate
libraries: numpy; pandas; scikit-learn; tensorflow and keras.

Each selected algorithm was trained and tested using different context
windows, optimizing the appropriate hyperparameters for each scenario using
the validation set. Hence, the training duration, in minutes, presented in
both Tables 3 and 4, accounts for the whole process, that is, training,
hyperparameter tuning, inference and metric computation.

Table 3: Forecasting results for analyser 6 (duration is presented in minutes). Algorithm | Window | MAE | RMSE | Duration
---|---|---|---|---
LSTM | 12 | 128.61 | 353.10 | 59.03
LSTM | 288 | 129.13 | 347.55 | 750.25
LSTM | 2016 | 129.69 | 340.89 | 4872.32
CNN | 12 | 133.46 | 347.34 | 18.41
CNN | 288 | 146.23 | 350.46 | 20.53
CNN | 2016 | 140.71 | 354.53 | 64.48
CNN-LSTM | 12 | 141.45 | 355.16 | 37.52
CNN-LSTM | 288 | 146.30 | 352.16 | 47.42
CNN-LSTM | 2016 | 138.54 | 343.41 | 253.98
TCN | 12 | 134.14 | 349.59 | 42.99
TCN | 288 | 129.80 | 347.21 | 312.73
TCN | 2016 | 130.57 | 340.30 | 3066.73

For analyser 6 (Table 3) and regarding the MAE, the LSTM algorithm
outperformed every other method for all window sizes, achieving the lowest
value, 128.61, for a window of 12 timesteps (one hour). Nevertheless, the RMSE
achieved by that same configuration is one of the highest, evidencing the
occurrence of large forecasting errors. In that sense, the TCN algorithm
achieved the best value of RMSE, 340.30, for a window of 2016 steps (one
week), while reaching a MAE of 130.57, slightly higher than the one presented
by the LSTM.

Table 4: Forecasting results for analyser 7 (duration is presented in minutes). Algorithm | Window | MAE | RMSE | Duration
---|---|---|---|---
LSTM | 12 | 43.98 | 116.38 | 74.65
LSTM | 288 | 45.2 | 115.86 | 722.07
LSTM | 2016 | 46.02 | 117.57 | 4216.95
CNN | 12 | 44.34 | 115.73 | 16.03
CNN | 288 | 45.75 | 114.11 | 26.67
CNN | 2016 | 49.64 | 114.90 | 55.77
CNN-LSTM | 12 | 43.80 | 115.23 | 36.17
CNN-LSTM | 288 | 45.35 | 114.85 | 51.78
CNN-LSTM | 2016 | 48.23 | 115.09 | 155.08
TCN | 12 | 42.75 | 114.06 | 59.99
TCN | 288 | 40.86 | 110.28 | 533.58
TCN | 2016 | 43.30 | 110.14 | 2523.57

On the other hand, for analyser 7 (Table 4), the best performing model in
terms of both MAE and RMSE, was the TCN, achieving the lowest value of MAE,
40.86, for a window of 288 timesteps (one day) and the lowest value of RMSE,
110.14, for a window of 2016 timesteps. The second and third best values for
both metrics were also achieved by the TCN algorithm, making it the clear
winner in the context of analyzer 7.

In terms of the context window, there is no evident correlation between the
window size and the values of MAE and RMSE. Nonetheless, it is apparent that
computational costs substantially increase for all algorithms as the number of
timesteps increases as well, being the processing time of both LSTM and TCN
considerably bigger than those presented by the remaining approaches. Finally,
it can also be concluded that computational-intensive algorithms seem to
contribute to better short-term instant energy consumption forecasting
capabilities, with the TCN, the second more expensive algorithm, being the
best performer overrall for both analysers 6 and 7.

##  5 Conclusion

In this work, the short-term predictive capabilities of four algorithms, a
LSTM, a CNN, a CNN-LSTM and a TCN, were evaluated and compared in a real
testbed in the manufacturing sector. It was possible to conclude that the TCN
was the best overall performer regarding both MAE and RMSE for the two energy
analysers considered in the study, which monitored the factory’s rapier loom
and cutting division for approximately two years. It was also possible to
conclude that the computational-intensive algorithms, LSTM and TCN, achieved
substantial performance gains when compared with both the CNN and the CNN-LSTM
at the price of increased compute duration.

According to the experimental results, there is no apparent correlation
between the size of the context windows and the values of MAE and RMSE, so,
further energy analysers from the testbed should be included in future works
in order to avoid selection bias and reach a more accurate conclusion.

## Acknowledgements

The present work has been developed under the EUREKA ITEA3 Project
CyberFactory#1 (ITEA-17032) and Project CyberFactory#1PT (ANI—P2020 40124) co-
funded by Portugal 2020. Additionally, this work has received funding from
FEDER Funds through COMPETE program and from National Funds through FCT under
the project SPET–PTDC/EEIEEE/029165/2017. Furthermore, this work has also
received funding from the project UIDB/00760/2020.

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