# A Survey of AI-Powered Mini-Grid Solutions for a Sustainable Future in Rural
Communities

Craig Pirie  Robert Gordon University  Harsha Kalutarage  Robert Gordon
University  Muhammad Shadi Hajar  Robert Gordon University  Nirmalie Wiratunga
Robert Gordon University  Subodha Charles  AltaVision Solar  Geeth Sandaru
Madhushan  AltaVision Solar  Priyantha Buddhika  AltaVision Solar  Supun
Wijesiriwardana  AltaVision Solar  Akila Dimantha  AltaVision Solar  Kithdara
Hansamal  AltaVision Solar  Shalitha Pathiranage  AltaVision Solar

(July 2024)

###### Abstract

This paper presents a comprehensive survey of AI-driven mini-grid solutions
aimed at enhancing sustainable energy access. It emphasises the potential of
mini-grids, which can operate independently or in conjunction with national
power grids, to provide reliable and affordable electricity to remote
communities. Given the inherent unpredictability of renewable energy sources
such as solar and wind, the necessity for accurate energy forecasting and
management is discussed, highlighting the role of advanced AI techniques in
forecasting energy supply and demand, optimising grid operations, and ensuring
sustainable energy distribution. This paper reviews various forecasting
models, including statistical methods, machine learning algorithms, and hybrid
approaches, evaluating their effectiveness for both short-term and long-term
predictions. Additionally, it explores public datasets and tools such as
Prophet, NeuralProphet, and N-BEATS for model implementation and validation.
The survey concludes with recommendations for future research, addressing
challenges in model adaptation and optimisation for real-world applications.

##  1 Introduction

Access to reliable and affordable electricity is a critical need for
communities around the world, particularly in rural areas where access to the
national power grid may be limited or non-existent. One potential solution to
this problem is the use of mini-grids, which are small-scale electrical
systems that generate and distribute electricity to a specific, often
localised, geographical area. Mini-grids can be powered by various energy
sources, including solar, wind, hydro, and diesel generators, and can be
designed to operate in conjunction with or independent of the national power
grid [4].

Mini-grids are a rapidly evolving technology due to the number of advantages
they provide over typical national grid systems. Firstly, mini-grids can
provide affordable and more reliable electricity to remote communities that
have no connection, or an unreliable connection to the national grid. The
rural nature of many communities means that mini-grid systems will often
heavily rely on clean energy sources such as solar and wind, reducing reliance
on depleting fossil fuels and thus improving environmental sustainability and
health equity. In cases where communities do have access to the national grid,
a local mini-grid will provide additional security and energy independence,
and potentially provide the ability to sell excess energy to the national grid
or neighbouring local grids [46]. Overall, access to cheap, reliable, and
cost-effective energy is a tremendous boon to rural communities, helping to
stimulate economic development through job creation and increasing household
income [5].

While prioritising the use of renewable energy sources is a noble and
necessary goal, many clean energy sources suffer from a high degree of
unpredictability due to how nature is inherently chaotic. Wind and solar
energy sources, such as wind turbines and solar panels, are significantly
affected by both short-term weather conditions and longer-term seasonal
variations [46]. Furthermore, the reliance on a mini-grid necessitates an in-
depth understanding of the energy demand within the community, encompassing
its fluctuations due to seasonal effects, human behaviour, and unforeseen
adverse conditions [47]. To ensure the smooth operation of a mini-grid,
accurate forecasting of both energy generation and energy demand becomes
crucial.

##  2 Forecasting Techniques: Technical Analysis

Forecasting is the process of predicting future events or trends based on
historical data and other pertinent information. It is a critical tool for
businesses, governments, and other organisations that need to plan for the
future and make informed decisions. Forecasting is used in a wide range of
applications, including finance, economics, weather forecasting, and supply
chain management [61].

Selecting an appropriate model is essential to achieving accurate forecasts.
This can be a difficult decision due to the large number of models to choose
from, each with its own strengths and limitations. The choice of model can
depend on factors such as the nature of the data, the forecasting horizon, and
the complexity of the relationships within the data.

Typically, AI-driven mini-grid systems consist of three core components, each
requiring accurate and timely forecasting:

  * •

A forecast for the energy supply from all distributed energy resources
connected to the mini-grid. Since the mini-grid will aim to be heavily reliant
on renewable resources, a successful forecast will require accurate
information on a variety of physical attributes, such as local weather
patterns, temperature, cloud cover, and other unspecific atmospheric effects.

  * •

A forecast for the energy demand from the consumers of the mini-grid. This
will require detailed knowledge of typical consumer behaviours and routines
within the region, as well as information on upcoming important regional
events.

  * •

A forecast for the optimal operation and management of the mini-grid is
essential. Once the energy supply and demand are forecast, a model must
determine the optimal approach to generating the required energy while meeting
established objectives, such as minimising carbon emissions. The model must
also forecast optimal times for grid management tasks, such as maintenance,
based on energy forecasts.

The following subsections will detail existing research on the types of models
and intelligent algorithms that could be utilised to achieve the
aforementioned core components, emphasising their suitability.

###  2.1 Statistical and Intelligent Techniques for Forecasting

This review focuses on mini-grid systems that utilise renewable energy, with
wind and solar as the primary distributed energy resources. Due to the
inherently chaotic nature of weather and the strong dependence of renewable
energy systems on current atmospheric conditions, forecasts for energy
generation from renewable power sources usually fall within a short or very
short forecast horizon [46].

Techniques used within forecasting typically fall into one of four categories
[25, 46]: physical prediction models [55, 33, 50, 18], statistical models [71,
66, 11, 41], intelligent computational algorithms [22, 36, 54, 23, 35], and
hybrid models [44, 62, 52].

The simplest type of forecasting model, typically used as a baseline for other
models, is called the persistence model. This model typically assumes that the
value at the next time-step will simply be equal to the value at the current
time-step [15, 31]. This model is considered naive since it doesn’t involve
any intelligent or statistical techniques and makes a base assumption of
continuity within the data. Slightly more complex persistence models can
factor in the gradient of change of the time-series which produced the current
time-step, and again assumes that the gradient will remain constant over time.

A physical forecasting model attempts to use the known laws of physics in
order to simulate future atmospheric conditions, and the corresponding weather
patterns, and thus forecast potential energy generation. The Numerical Weather
Prediction (NWP) model is widely regarded as the prevailing technique for
physical modelling within weather forecasting [42, 19, 37]. The NWP model
typically functions by dividing the atmosphere into a three-dimensional grid
of cells, with each cell containing information about the temperature,
pressure, wind speed, and humidity at that location. The model then relies on
the physical theories of thermodynamics and fluid dynamics to simulate the way
these variables will interact and change over time and predicts how the
weather will evolve [13]. Fundamental challenges to NWP models include the
expertise in mathematics and physics required for their development and
effective utilisation, alongside the required substantial computational power
and resources, potentially restricting access for smaller or less developed
countries [67, 13].

Statistical models are widely used techniques for forecasting time-series data
due to their relative simplicity and ease of implementation [45]. These models
typically utilise statistical techniques such as Auto-regression (AR) and
Moving-averages (MA), or a combination of both (ARMA) [48]. Auto-regression is
a common time-series analysis method that aims to model the relationship
between a time-series and its past values, utilising techniques such as
Maximum Likelihood Estimation (MLE) to minimise the error between the
predicted and actual values [20, 46]. Moving-averages involve taking an
average of residuals over a moving time window in order to estimate the
average size and direction of the errors in the forecast. By incorporating an
integrated term into the ARMA model, the new ARIMA model [12] can account for
both the long-term trends or patterns in the data (represented by the AR term)
and the random fluctuations around those trends (represented by the MA term),
while also accounting for non-stationarity in the data. The result is a
powerful modelling framework that can capture the complexity of time series
data and produce accurate forecasts [32].

The use of deep learning techniques in forecasting models has gained
popularity due to their capability to effectively handle complex and non-
linear relationships between variables. These intelligent forecasting models
also have the advantage of relying less on knowledge of any underlying
physical processes, instead utilising a more black box approach [46]. One of
the most widely used AI techniques within forecasting includes the use of
fuzzy logic [46].

Fuzzy logic can model complex relationships between input variables and
forecast values. Within a fuzzy logic model, values are represented as a
degree of membership to a linguistic category, rather than a simple crisp
value [9, 43]. Fuzzy logic is useful for modelling relationships which are
uncertain, imprecise, or difficult to quantify using traditional statistical
methods, especially if the data is noisy or incomplete [56]. Fuzzy logic works
by first defining the input and output variables in terms of their fuzzy sets
and membership functions. For example an input variable of temperature could
be defined by categories temperature_low, temperature_medium, and
temperature_high, each of which is defined by a membership function. An
example of such a variable with its member functions is shown in Figure 1
below.

Figure 1: An example of how the temperature variable can be defined by a fuzzy
set of category memberships

Rules can then be established which define relationships between the input and
output memberships. These rules typically take the form of if/then statements
such as IF temperature is high AND humidity is high THEN rain is high. The
rules are often generated using expert knowledge and can be combined to form a
fuzzy inference system that can generate forecasts. Given new test data, the
fuzzy inference system could provide a rain forecast by observing the degree
of membership of the input values to each output category, or a process called
defuzzification could be used to produce a crisp real-world value which could
be better interpreted [56]. Issues with simple fuzzy models stem from the
rules and fuzzy sets which govern them. For complex systems, these rules and
sets will require expert knowledge to craft, and an abundance of rules could
easily lead to the model overfitting on any training data.

Support vector machines (SVMs) are a type of supervised machine learning
algorithm used for classification and regression analysis [58]. SVMs work by
attempting to produce a hyperplane, known as a decision boundary in
classification problems, which separates the data into two or more classes
based on their features. A hyperplane is simply a surface that is defined
within the feature space which has the largest margin possible between the
classes defined within the training data. To assist with finding the
hyperplane a technique called the Kernel Trick is often used. This trick aids
in defining complex non-linear decision boundaries through the use of a kernel
function which maps the input data onto a higher-dimensional feature space in
which the data is linearly separable [58]. Figure 2 demonstrates the same
input data in different dimensional feature spaces.

Figure 2: Left: RBF kernel with non-linear boundary. Right: Linear kernel with
linear boundary.

Once the decision boundary has been defined, new input data can be classified
into the appropriate class depending on its position relative to the
hyperplane. Advantages of using the SVM technique include the ability to work
in higher dimensional feature spaces, which is beneficial for performing
forecasts with many features. Solving the optimisation problem of the decision
boundary also makes SVM models robust to outliers and overfitting. When
training, the model attempts to maximise the distance between the hyperplane
and the closest data points from each class, known as the support vectors,
whilst also applying a cost for incorrect classifications. Since this cost
decays with distance from the hyperplane, notable outliers do not have much
effect on the decision boundary [39].

Another prominent machine learning model is the Artificial Neural Network
(ANN). Loosely inspired by the structure and function of the human brain, an
ANN consists of layers of interconnected nodes (neurons), where each neuron
takes input from one or more neurons in the previous layer, performs a simple
computation (usually a weighted sum followed by a non-linear activation
function), and passes the output to one or more neurons in the next layer.
Using a training algorithm such as back-propagation, artificial neural
networks can adjust the weights on the connections between neurons to
approximate complex input-output mappings. Some common training algorithms are
as follows:

  * •

CMA-ES: Evolutionary algorithm for continuous optimisation, adaptively models
correlations between variables, effective in complex landscapes.

  * •

Back-propagation: Neural network training algorithm, calculates gradients by
propagating errors, adjusts weights and biases to minimise the loss function,
widely used for training deep learning models.

  * •

Adam [69]: Optimisation algorithm for neural network training, combines
adaptive learning rate and momentum, efficiently handles sparse gradients,
accelerates convergence — a popular choice for various deep learning tasks.

Through careful choice of the NN architecture and choosing the most
appropriate training algorithm, an ANN can learn to accurately make
predictions on new, unseen data [2].

Figure 3: An example of the structure of a simple neural network featuring 2
hidden layers

ANNs are powerful due to their ability to capture complex nonlinear
relationships between input and output variables, making them well-suited for
forecasting tasks where patterns in the data may be difficult to identify.
Depending on the structure of the neural network, it is capable of capturing
many obscure patterns and seasonalities without requiring expert knowledge of
the subject matter. The challenge, however, lies in the fact that without
experience in constructing complex ANNs, determining the optimal structure,
training algorithms, and tuning methodology can be difficult. Depending on the
purpose of the model, an ANN’s lack of transparency and interpretability could
pose an issue if information on how the model makes predictions is essential.

The aforementioned ANN is typically referred to as a Feed-Forward Neural
Network since the information flows in a single direction from input to output
without and loops. A limitation of this architecture is the inability to
capture temporal dependencies within the data. To assist with this, a more
complex network design can be implemented which features a new recurrent
connection within each hidden node. This feedback loop allows for the network
to better process sequential data by using the output from the previous step
as an input to the current step. This type of architecture is known as a
Recurrent Neural Network (RNN) and provides the benefit of allowing the model
to better capture details of the current time step in context of previous time
steps. This is particularly useful for handling sequential data such as in
time-series forecasting.

Figure 4: Illustration of an RNN being unfolded across three time steps, where
the recurrent connections are expanded to show the flow of information from
input ($x$) to hidden state ($h$) and output ($o$) at each time-step, with
weight matrices $U$, $W$ and $Z$ shared across all steps [24].

Even though RNNs are more capable at capturing dependencies from sequential
data sources, they struggle to fully capture dependencies which take a long
time to appear due to the vanishing gradient problem [27]. The vanishing
gradient problem refers to the issue in deep neural networks where the
gradients diminish exponentially as they propagate backwards through layers,
hindering the learning of long-term dependencies and resulting in ineffective
weight updates. To help mitigate this issue, an architectural design called
Long short-term memory (LSTM) can be used as shown in 5.

Figure 5: An example of an LSTM cell featuring forget, input, and output gates

The input gate, forget gate and output gate control the flow of information,
allowing LSTMs to selectively retain and update relevant information over
time. By learning to adjust these gate values during training, LSTMs can
capture and retain important context from past time steps while disregarding
irrelevant details. This mechanism enables LSTMs to make accurate predictions
and model complex temporal patterns in tasks like language processing, or time
series analysis.

Another popular technique which has applications within forecasting is Random
Forest. Random Forest is a powerful machine learning algorithm that functions
by combining the predictions from multiple decision trees to make a more
accurate prediction. The ensemble of decision trees is generated from subsets
of the training data using a process called bagging. The outputs from all
decision trees are aggregated to form a final prediction, often created from
the average value of trees. Random forest techniques are popular due to their
ability to handle large datasets of complex multidimensional data, capture
non-linear relationships, and provide robustness against noise and outliers
[6]. Even though these models can be powerful forecasting tools on their own
if utilised properly, they are often combined in an attempt to harness their
combined strengths and mitigate their weaknesses. These are known as hybrid
models and typically consist of a combination of statistical and machine
learning models [51, 7, 46].

One of the most common hybrid models is the Adaptive Neuro-Fuzzy Inference
System (ANFIS), which is a hybrid model that combines the learning
capabilities of ANN and the interpretability of fuzzy logic. The ANFIS
structure closely resembles the flow of a typical fuzzy model, with each stage
of progression being represented by layers of neurons [63]. Figure 6 below
demonstrates an example structure of an ANFIS system.

Figure 6: A simple ANFIS architecture

The ANFIS architecture typically consists of 5 layers. The first layer is the
input layer, which receives input values and passes them to the next layer.
The second layer is the membership function layer, or fuzzification layer,
which calculates the membership value of the input variables to each fuzzy
set. The third layer is the rule layer, which calculates the firing strength
of each rule by taking the product of the membership values of the input
variables. The fourth layer is the normalisation layer, which normalises the
firing strengths. Finally, the fifth layer is the output layer, which
aggregates the firing strengths of all the rules and generates the final
output value [73].

In the context of energy forecasting, ANFIS can be particularly useful due to
its ability to model complex, non-linear relationships between variables such
as weather conditions, historical energy consumption data, and other relevant
factors. By continuously learning from new data, ANFIS can adapt to changes in
patterns, improving the accuracy of its predictions over time.

##  3 Forecasting Techniques: Implementations

When moving from theory to implementation, the effectiveness of each
forecasting technique is heavily influenced by the exact parameters of the
forecasting problem, especially the data and resources available. As
demonstrated in this section, no single forecasting technique excels in all
scenarios, especially when considering the desired forecasting horizon. The
forecasting horizon denotes the time duration for which a forecast is made,
representing a future period over which predictions are generated. Although
names and definitions may vary, the commonly accepted forecasting horizons
include:

  * •

Very short-term. From a second to 30 minutes.

  * •

Short-term. From 30 minutes to 6 hours.

  * •

Medium-term. From 6 hours and 1 day.

  * •

Long-term. From 1 day to 1 week.

Knowledge of the required time horizon is extremely important when searching
for an appropriate forecasting technique, and the choice is essentially an
optimisation problem regarding the required responsiveness and accuracy of the
model.

When exploring models for a particular forecasting problem, it is important to
be able to determine whether the model has a suitable accuracy to meet the
desired forecasting objectives and requirements. Many metrics can be employed
to determine the accuracy of a forecasting model, a description of some of the
most common metrics are as follows.

  * •

Mean Absolute Error (MAE) [57, 70, 26, 60]. Measures the average absolute
difference between the predicted values and the actual observed values. MAE is
a simple and easy interpret metric and is robust to outliers in the data,
however the inability to determine the direction and bias of the errors can
cause an issue depending on the forecasting problem.

$MAE=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right|$

  * •

Mean Squared Error (MSE) [57, 60]. Calculates the average of the squared
differences between the predicted values and the actual observed values. MSE
is useful when more emphasis needs to be placed on larger errors, for example,
if risk aversion is especially important. Due to the squaring nature of the
metric, the error has different units from the original data which could
potentially harm interpretability.

$MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}$

  * •

Root Mean Squared Error (RMSE) [57, 70, 60, 26]. Calculates the square root of
the MSE. Useful for interpretability due to having the same units as the
original data.

$RMSE=\sqrt{MSE}$

  * •

Mean Absolute Percentage Error (MAPE) [57, 70, 60, 26] Calculates the average
percentage difference between the predicted values and the actual observed
values. Useful for when there is a need to compare accuracy across data sets
with different scales since it provides a relative measure, however, the error
metric can become undefined when the data contains zero values since the error
will become undefined.

$MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right|\times
100$

  * •

Symmetric Mean Absolute Percentage Error (SMAPE) [21, 60] A variation of MAPE
which divides by the sum of the forecast value and actual value, instead of
just the actual value. This allows the metric to better deal with zeroes in
the data compared to MAPE, although undefined values can still occur.

$SMAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|y_{i}-\hat{y}_{i}\right|}{(|y_{i}|+|\hat{y}_{i}|)/2}\times
100$

In these equations, $n$ represents the total number of data points, $y_{i}$
represents the observed or actual values, and $\hat{y}_{i}$ represents the
corresponding forecast or predicted values. To summarise, the choice of metric
is important and heavily dependent on the individual forecasting problem.

###  3.1 Energy Generation Forecasting

Mini-grids usually rely on renewable energy sources, such as wind and solar,
and a backup diesel generator to provide power. Due to the weather dependency
of the renewable assets, predicting the power available at a future time will
require accurate information on future weather conditions, as well as the
availability of the energy assets [34].

Figure 7: Creating a power generation forecast

As such, to have a viable mini-grid, it is important to accurately determine
the energy supply in both the short-term and the medium/long-term. Short-term
forecasts are highly beneficial for supporting near real-time supply and
demand balancing, thus helping to maintain grid stability. Accurate long-term
forecasts would provide the ability to provide more operational security in
regards to ensuring sufficient energy reserves, along with allowing for more
risk-free maintenance scheduling [59].

Multiple statistical and intelligent forecasting models for short-term
renewable energy generation (solar) and consumption were examined by Abu-Salih
B et al. [3], and their empirical results were compared. The study collected
real-time energy data from smart meters installed in residential premises in
Western Australia covering energy import and export from the grid, rooftop
photo-voltaic (PV) energy generation, household energy consumption, and
outdoor temperature. Taking 11 months of data, splitting into 9 months
training and 2 months testing, they pre-process the data through
transformation into 1-hour intervals, as well as integrating separate
temperature data from the Bureau of Meteorology for Perth, and adding a new
day-of-week feature. For multivariate forecasting using supervised deep
learning, the data was split into input features and an output label. The
input features included temperature, day of the week, and energy consumption
or generation at the previous time step, while the output label represented
the current energy consumption or generation. Additional steps were taken to
ensure that the data was stationary and appropriately scaled by calculating
the min/max coefficients of all features. The team then performed extensive
testing using a large number of linear/statistical models (ARIMA, Linear
Regression), as well as a number of machine learning models (ANN, SVM, Random
Forest), and a novel optimised LSTM model. The team observed that statistical
techniques generally outperformed AI techniques in their study. For instance,
Linear Regression achieved an approximate Mean Absolute Error (MAE) of 1.57
and Root Mean Square Error (RMSE) of 2.11 for energy generation (EG) and
consumption (EC). In contrast, an Artificial Neural Network (ANN) model had an
MAE of 8.687 for energy generation (EG) and 5.331 for energy consumption (EC),
with RMSE values of 12.742 (EG) and 7.353 (EC). The optimised Long Short-Term
Memory (LSTM) model developed by the team outperformed all other techniques,
achieving an MAE of 0.329 (EG) and 0.241 (EC), and RMSE of 0.5654 (EG) and
0.3273 (EC).

Mason et al. [49] examine forecasting energy demand, wind generation, and
carbon dioxide emissions in Ireland using evolutionary neural networks. The
study utilises the covariance matrix adaptation evolutionary strategy (CMA-ES)
as an optimisation algorithm to train neural networks for short-term
predictions and evaluate accuracy against other NN optimising strategies, as
well as a small number of statistical models. The team chose to use recurrent
neural networks (RNNs) over feed-forward networks due to their ability to
retain memory of previous time steps, making them an appropriate choice for
time-series forecasting problems. The RNN architecture used in the study
contains six hidden neurons and two inputs representing the current and
previous time steps within the time-series. The networks are fully connected,
and their output corresponds to the prediction for the next time step. Seven
algorithms, including CMA-ES, particle swarm optimisation (PSO), differential
evolution (DE), back-propagation (BP), moving average (MA), random walk
forecasting (RWF), and linear regression (LR), are evaluated for forecasting
accuracy using four standard metrics: mean absolute error (MAE), mean squared
error (MSE), root mean squared error (RMSE), and mean absolute percentage
error (MAPE). Each network was trained on one month of data and tested against
the following month of data. Each data set consists of time-series data in
increments of 15 min, totalling 2878 data points. CMA-ES demonstrates superior
performance in terms of convergence rate and accuracy compared to all other
algorithms. It achieves the highest prediction accuracy across all three
forecasting problems (wind generation, energy demand, carbon dioxide
emissions). It was also noted that Linear Regression performs surprisingly
well in predicting wind power generation but struggles with power demand due
to its complex non-linearities and frequent steep gradients.

Chen, Gooi, & Wang [17] introduce a solar radiation forecast technique that
combines fuzzy logic and neural networks to achieve accurate predictions in
various weather conditions. The proposed approach leverages future sky
conditions and temperature information obtained from the National Environment
Agency (NEA) to forecast solar radiation. The researchers emphasise the
notable influence of factors such as sky condition, temperature, and time
information on the received solar radiation by photovoltaic (PV) cells. To
perform the forecasts, a feed-forward neural network (FNN) is employed,
utilising historical solar radiation and weather data for training. The input
layer comprises past solar radiation, temperature, and sky information, while
the output layer predicts the solar radiation for the subsequent hour. During
the training process, historical data is utilised for each hour of the
daytime, classifying sky conditions into categories such as sunny, rainy, and
cloudy for each hour. The temperature variable is employed to discern the
differing solar radiation patterns under distinct sky conditions. The
determination of parameters for each neural network is achieved through an
algorithm, and approximately 21,600 data points are employed for training,
validation, and testing purposes. The effectiveness of the proposed approach
is validated through a case study encompassing four distinct scenarios (sunny,
cloudy, rainy, and normal). The study demonstrates a significant reduction in
the Mean Absolute Percentage Error (MAPE) compared to other existing solar
radiation forecasting methods including an ARIMA model, as well as individual
fuzzy and neural network models.

Li [40] developed a neural network-based prediction model to estimate the
power produced by individual turbines at the Fort Davis wind farm using data
collected from each turbine. The authors use data collected from the Fort
Davis wind farm, and develop a four-input neural network, using wind speed and
direction from two sources, that outperforms a traditional single-parameter
model. The study found that wind velocity has a significant impact on power
output, while wind direction has less influence, particularly at higher power
generation levels. A separate neural network was developed for each turbine to
predict its performance, as this approach reduces the size and complexity of
the neural network and scales better for large wind farms. The input-output
mapping was modelled using a multi-layer perceptron, and the resulting neural
network is shown to estimate wind power generation efficiently as a diagnostic
tool, with the estimated power output closely matching the measured power
output, in contrast to the traditional model’s estimates.

In a 1998 study [8], the authors present a method for forecasting wind speed
and electrical power generated by wind turbines, specifically for power
systems with integrated wind parks. Artificial neural network models were used
to forecast average values of wind speed and corresponding electrical power
for 10-minute or 1-hour intervals using wind speeds and their derivatives as
inputs. The team used data collected over seven years from six sites on
islands of the South and Central Aegean Sea in Greece for testing. Auto-
correlation patterns were discovered, revealing daily, monthly, and season-
long periodicities. Recent wind speeds at the site and neighbouring wind
speeds directed towards the site were found to be the best inputs for the
model, leveraging spatial correlation. The team found better success using
differenced wind patterns, and a simple ANN was used for forecasting,
producing satisfactory results.

###  3.2 Energy Demand Forecasting

Accurately forecasting the energy demand of mini-grid consumers is essential
for any mini-grid management solution. This accurate forecasting is crucial
for determining whether the energy generated by the grid’s renewable resources
will be sufficient to meet demand at all times. This is especially important
since the grid’s backup diesel generators will most likely require preparation
and startup time before it can effectively meet energy demands. Combined with
energy generation forecasts, it will become easier to provide a more reliable
grid and better forecast grid maintenance.

In a study by Bahrudin Hrnjica and Ali Danandeh Mehr [30], a specialised
version of the recurrent neural network (RNN), namely long short-term memory
(LSTM), is proposed as an efficient alternative for an energy prediction model
in Nicosia, the capital of Cyprus. The proposed approach utilises a deep
learning LSTM model combined with an auto-encoder for unsupervised learning.
The model is fed with 15 input features, consisting of power demand values
from previous time steps. The LSTM layer is followed by a 30% dropout layer,
and produces a single output. The LSTM model was trained and validated on data
from January 2011 to November 2016, with the December 2016 values used as a
test set. The study compared their LSTM deep learning model to a TSD (Time
Series Decomposition) model, which utilised the Prophet [61] forecasting
package for additive modelling and seasonal decomposition. The TSD model [68]
incorporated various forecasting methods, such as ARIMA and exponential
smoothing, to obtain the best possible model. The energy demand dataset from
January 2011 to September 2015 was used for building the TSD model, while the
period from October 2015 to December 2016 served as the validation and testing
dataset. Results from their study showed that the LSTM deep learning model
they developed outperformed the Prophet TSD model in various forecast
horizons. Specifically, the LSTM model achieved lower root mean squared error
(RMSE) values compared to the TSD model for 5-day forecasts (198 vs. 353),
2-week forecasts (320 vs. 386), and 1-month forecasts (281 vs. 720). This
indicates the superiority of the LSTM approach in accurately predicting energy
demand over multiple time intervals and the potential of LSTM-based approaches
for improving energy prediction models in smart cities.

Tao Hong, Min Gui, Mesut E. Baran, and H. Lee Willis [29] propose using
multiple linear regression for modelling and forecasting short-term electric
load. The paper explores the causal factors driving the fluctuations of
electric load and their impacts on energy purchase and demand side management
(DSM). The authors observe from the data that there are clear seasonalities
within each day, week, and year, heavily influenced by temperature and human
activities. The regression model proposed by the team was used to generate a
3-year hourly demand forecast and was shown to have significantly improved
performance with a MAPE of 4.558% over the existing mid-term load forecast
used by an unspecific US utility company.

In a paper by Guan et al. [28], the authors explore Very Short-Term Load
Forecasting (VSTLF) methods for predicting electric power system loads a few
minutes into the future using Wavelet Neural Networks trained by Hybrid Kalman
Filters (WNNHKF). The team incorporates wavelet analysis and hybrid Kalman
filters into the model which forecast loads one hour into the future in
5-minute steps using a moving window approach. The performance of different
models was evaluated in terms of Mean Absolute Percentage Error (MAPE) for
5-minute and 60-minute load forecasts:

  * •

Persistence: 0.38 MAPE (5 minutes), 4.29 MAPE (60 minutes)

  * •

Linear AR: 0.16 MAPE (5 minutes), 1.73 MAPE (60 minutes)

  * •

WNN: 0.08 MAPE (5 minutes), 0.49 MAPE (60 minutes)

  * •

WNNHKF: 0.12 MAPE (5 minutes), 0.47 MAPE (60 minutes)

The results demonstrate that the WNNHKF model developed by the team
outperforms other methods, achieving lower MAPE values for both short-term and
one-hour load forecasts.

Ahmad et al. [6] developed several machine learning models for predicting the
energy consumption of a large hotel in Madrid. They compared the performance
of a feed-forward back-propagation artificial neural network (ANN) with a
random forest (RF) model. The study utilised a dataset of 5-minute historical
values of Heating, Ventilation and Air Conditioning (HVAC) electricity
consumption, along with weather conditions and hotel occupancy data. The
results indicated that the ANN model outperformed the RF model on various
performance metrics with a MAPE of 4.97 vs 6.03 for the RF model. However, the
team noted that the RF model demonstrated the ability to handle missing values
effectively due to its inherent ensemble-based nature. Overall, they conclude
that both models showed strong non-linear mapping generalisation ability and
could be effective in predicting energy consumption in the problem area.

Allee et al. [10] conducted a study on estimating electricity demand for un-
electrified customers in the context of the design of mini-grid systems. The
team notes that accurately estimating electricity demand is crucial for site
selection, revenue projection, and sizing system components in order to
minimise capital costs. The researchers tested a data-driven approach using
survey and smart meter data from 1378 Tanzanian mini-grid customers. Their
findings revealed that machine learning models incorporating customer survey
data consistently outperformed baseline models. The best-performing model,
LASSO regression, achieved a median absolute error of 66% and 37% for
individual connections and mini-grid sites, respectively. The study highlights
the importance of thorough inventories of customers’ currently-owned
appliances rather than relying on detailed demographic information or self-
reported habits and plans. They conclude by stating that by combining
shortened questionnaires with smart meter data, accurate estimates of initial
customer electricity demand can be significantly improved compared to standard
field practices.

A study conducted by Cevic and Cunkas [72] involved short-term load
forecasting using fuzzy logic and adaptive neuro-fuzzy inference system
(ANFIS). The team analysed several years of historical data and noted the
unique load characteristics of different parts of the week. The inputs for
their ANFIS forecasting model included historical load, temperature
difference, and season. Unlike previous studies, they performed hourly load
forecasts for an entire year and demonstrated that fuzzy logic yielded good
results for large datasets over an extended period. The study utilised
electricity consumption data from the Turkish Electricity Transmission Company
(TEIAS), for the years 2009 to 2012. The authors emphasised the influence of
time factors on load curves, which vary with season, week, day, and hour.
Meteorological variables, particularly temperature, were identified as crucial
factors affecting short-term load forecasting. The study proposed fuzzy logic
and ANFIS models for load forecasting and compared their results with actual
load data. The inputs considered for the forecasting system included
historical load, temperature difference, and season. Season was included
because changes in temperature can have opposite effects on load depending on
the time of year. The results indicated that both ANFIS and fuzzy logic
methods produced accurate forecasts, with the ANFIS model exhibiting better
forecasting accuracy, as evidenced by lower mean absolute percentage errors
(MAPEs) of 1.85% compared to 2.1% for fuzzy logic, based on data from the year
2012.

###  3.3 Energy Management Forecasting

The forecasting of mini-grid management and operations plays a crucial role in
ensuring the reliable and efficient functioning of mini-grid systems. Accurate
operation forecasts would enable mini-grid operators to optimise the
deployment and scheduling of all energy resources, better plan maintenance
activities, ensure grid stability and make informed decisions regarding energy
procurement and storage capacity. Reliable forecasting would minimise
operational costs, lower carbon emissions by reducing reliance on diesel
backup generators, and ultimately enhance the overall reliability and
sustainability of the mini-grid system.

Chaouachi, Kamel, Andoulsi, and Nagasaka [16] demonstrate research into
solving multi-objective intelligent energy management problems within micro-
grids, especially addressing decision problems with conflicting objectives.
The optimal solution aims to minimise the operation cost and gaseous emissions
while maintaining load balance within the power generation limits of each
controllable distributed generator (DG). The cost objective function considers
power generation costs, energy trade between the micro-grid and the main grid,
and maintenance costs proportional to DG power generation and storage. The
emission objective function incorporates individual DG emission rates during
autonomous generation and the average emission rate of the main grid in case
of a generation shortfall, as well as the fact that selling power to the grid
reduces overall emissions. The team note that battery scheduling is an
integral part of achieving the optimal energy management objectives. An expert
system based on fuzzy logic is developed for micro-grid battery scheduling,
complemented by multi-objective linear programming optimisation for cost and
emission considerations. The fuzzy logic system relies on seven measured and
forecast parameters designed using expert knowledge. These parameters include
time, battery state of charge, electricity price, load demand, renewable
energy generation, and weather forecasting. The team then defined a set of
fuzzy rules to determine optimal battery charge or discharge rates based on
the state of the aforementioned parameters. As an example, charging
prioritises low load demand periods and high renewable energy availability,
while discharging aims to meet load balance without exceeding depth-of-
discharge limits or requiring expensive energy purchases. The proposed
methodology demonstrates significant cost savings during peak load demand
periods and non-availability of solar generation. Simulation results show an
approximate 4.5% cost savings and 3% lower emissions compared to conventional
approaches, highlighting the effectiveness of the proposed multi-objective
intelligent energy management system.

Forecast |  Source |  Model Type |  Features
---|---|---|---
Energy Generation Forecasting |  [49] |  RNN using CMA-ES |  Current and previous time-steps
[40] |  ANN |  Wind speed and direction
[8] |  ANN |  Wind speed and direction
|  [16] |  RNN |  Max wind speed, past 3 wind speed, wind direction, pressure, humidity
Energy Supply Forecasting |  [17] |  ANFIS |  Past solar radiation, temperature, sky information
[16] |  RNN |  Max temp, min temp, temp, pressure, precipitation, solar irradiance
[3] |  Optimised LSTM |  Temperature, day of the week, energy generation at previous time-step
|  [30] |  LSTM + Auto-encoder |  15 previous energy demand time-steps
|  [28] |  WNNHKF |  12 previous energy demand time-steps
|  [6] |  FFNN |  current energy demand, weather conditions, hotel occupancy
|  [10] |  LASSO regression |  Smart meter data, customer asset estimation
|  [72] |  ANFIS |  Historical load data, temperature, season
Energy Management Forecasting |  [16] |  Fuzzy Logic |  Time, battery state of charge, electricity price, load demand, renewable energy generation, and weather forecasting
Table 1: A summary of existing research and models

##  4 Overview and Recommendations

In this section, we implement two time-series forecasting techniques
identified as appropriate during our earlier analysis of the literature. We
also discuss the utility of open-source energy demand and supply forecasting
datasets for model exploration and transfer learning.

###  4.1 Public Datasets for Testing

Since it will take time to install and generate real data from the project
mini-grid, any model development will require large amounts of relevant real-
world data or at least accurate simulated data.

####  4.1.1 Electrical consumption, generation, pricing, and weather data from
Spain

This dataset, available at
https://www.kaggle.com/datasets/nicholasjhana/energy-consumption-generation-
prices-and-weather, contains 4 years of electrical consumption, generation,
pricing, and weather data for Spain with a 1-hour frequency. The data is
contained within two files energy_dataset.csv and weather_features.csv, each
of which is described in more detail below.

energy_dataset.csv Contains 29 features including the amount of energy in MW
from a variety of energy sources such as coal, wind, solar, hydro, and gas,
along with the total energy load at that time and the price of energy at that
time.

weather_features.csv Contains 17 features including the min/max/current
temperature, pressure, humidity, wind speed and direction, for the 5 largest
cities in Spain.

The dataset’s available features correspond to the inputs commonly used in
energy generation and consumption models studied during the research. However,
it is important to consider potential challenges associated with the data. One
such challenge stems from the dataset covering a broad geographical area,
rather than a localised mini-grid setting. Additionally, variations in climate
trends between Spain and the target country, as well as distinct cultural
practices and holidays specific to each region, may pose additional
considerations.

The dataset is also potentially limited by the lack of information on Carbon
emissions from the various energy sources, as well as information regarding
energy trading prices. Since energy forecasting involves a multi-objective
optimisation problem aimed at balancing cost, carbon footprint, and hardware
operations and maintenance, additional external information needs to be
incorporated into this dataset to achieve these objectives.

####  4.1.2 Appliances Energy Prediction

The “Appliance Energy Prediction” dataset available on the UCI repository [14]
can be used to fit forecasting models for energy usage in household
appliances. The dataset focuses on the energy consumption and prediction for a
low-energy house located in Stambruges, Belgium, approximately 24 km from the
City of Mons. The house was completed in December 2015 and features new
mechanical systems. Designed according to passive house certification
standards, the house aims for an annual heating and cooling load of no more
than 15 kWh/m² per year, as determined by the Passive House Planning Package
(PHPP) software.

Key features of the house include a wood chimney for heating, with monthly
logs of the type and amount of wood used, and a measured air leakage rate of
0.6 air changes per hour at 50 Pa. The house has highly insulated exterior
walls, roof, and ground (U ¡ 0.1 W/m² K), and triple-glazed windows (Ug = 0.5
W/m² K, Uf ¡ 0.9 W/m² K). Ventilation is provided by a heat recovery unit with
90-95% efficiency. The total floor area is 280 m², with a heated area of 220
m². The house’s façade is oriented +10° (Southwest) from due South. Typically,
the house is occupied by four individuals—two adults and two teenagers—with
one adult working regularly from a home office.

The dataset includes 29 features related to household energy consumption. Key
variables include timestamps, energy use for appliances and lights,
temperature and humidity measurements in different rooms, and weather
conditions such as outdoor temperature, atmospheric pressure, humidity, wind
speed, visibility, and dew point. Additionally, two random variables are
included for regression model testing.

For energy demand modelling, this dataset could be a valuable asset for
benchmarking the efficacy of various forecasting techniques. Additionally, it
could prove a useful source to extract fundamental energy usage patterns which
can later be used to improve target models through transfer learning.

###  4.2 Building Energy Efficiency

The Energy Efficiency Dataset [65] aims to predict the heating and cooling
load requirements for buildings. It includes features such as relative
compactness, surface area, wall area, roof area, overall height, and
orientation. Furthermore, it accounts for the glazing area and its
distribution. These features reflect architectural and environmental
characteristics influencing a building’s energy efficiency. The target
variables in the dataset are the heating and cooling loads, which are critical
for understanding and improving energy consumption patterns in buildings.

This dataset is highly relevant for optimising mini grids by enabling the
development of predictive models for energy distribution and storage. Accurate
predictions of heating and cooling loads help in planning and balancing the
energy demand within mini grids. This ensures efficient utilisation of
resources, supports the integration of renewable energy sources, and enhances
the sustainability and reliability of energy supply in small-scale power
systems.

###  4.3 Smart Grid Smart Meter

The Smart Grid Smart Meter Data dataset [1] is designed for the purpose of
bias correction of next-day maximum and minimum air temperature forecasts from
the Local Data Assimilation and Prediction System (LDAPS) model [38] operated
by the Korea Meteorological Administration. The dataset contains fourteen
numerical weather prediction (NWP) meteorological forecast variables, two in-
situ observations, and five geographical auxiliary variables. It covers summer
data from 2013 to 2017 over Seoul, South Korea. The input data includes next-
day forecasts from the LDAPS model, present-day in-situ maximum and minimum
temperatures, and various geographic variables. The outputs are the next-day
maximum and minimum air temperatures. Hindcast validation — statistical
calculations determining probable past conditions — was conducted for the
period from 2015 to 2017.

The dataset comprises weather station data, present-day temperature records,
and various meteorological forecasts, such as relative humidity, temperature
lapse rates, wind speed, latent heat flux, cloud cover, and precipitation for
different periods. It also incorporates geographic features like latitude,
longitude, elevation, slope, and solar radiation.

By leveraging this dataset, mini grid operators can refine their energy
management strategies. Accurate temperature predictions help in forecasting
energy demand, crucial for balancing supply and ensuring efficient energy
storage. This optimisation is vital for integrating renewable energy sources
and enhancing the sustainability and reliability of mini grids. The dataset’s
comprehensive coverage of weather and geographic variables allows for detailed
analysis and improvement of energy usage predictions, leading to better
resource allocation and a more resilient energy system.

###  4.4 Examination of Additional Public Tools

As well as the existing research covered within Section 3, an examination was
also performed on a small number of existing tools and software which could
help facilitate forecasting.

####  4.4.1 Prophet

An article by Taylor and Letham [61] describes what they call “forecasting at
scale,” where they design a highly configurable decomposable time-series
model. This model features interpretable parameters such as trend,
seasonality, and holidays, which can be easily understood and adjusted using
expert knowledge. Made available through the Facebook Open Source project, the
Prophet forecasting model is easily accessible through a Python installation
and provides the following features:

  * •

Prophet models the trend component using a piece-wise linear function allowing
it to accurately model changes in trends over time.

  * •

Complex seasonal components such as daily, weekly, or yearly patterns can be
captured using Fourier series analysis, and custom seasonalities can be added
if known.

  * •

Prophet can handle holiday components that are affecting the time series, such
as national holidays, using a built-in library of holidays for many countries
and cultures.

  * •

Provides interactive forecasts very quickly.

Figure 8 below demonstrates the capability of the Prophet forecasting tool in
automatically determining trends within the data. Prophet was assigned the
task of analyzing a one-year dataset of solar energy generation and
identifying the variations in energy generation throughout the day and week.

Figure 8: Initial examination of the solar energy test-data using Prophet
time-series decomposition

####  4.4.2 NeuralProphet

NeuralProphet is an open-source time series forecasting library developed by
Facebook’s Core Data Science team. It is built on top of the popular PyTorch
library and provides a user-friendly interface for creating and training
neural network models specifically designed for time series forecasting tasks.
NeuralProphet extends the traditional Prophet forecasting model by
incorporating a neural network architecture into its framework, allowing it to
better capture complex nonlinear relationships and patterns in time series
data. Key features of NeuralProphet include:

  * •

NeuralProphet allows users to define various components of a time series
model, such as trend, seasonality, and events, providing flexibility in
modelling different types of time series data.

  * •

NeuralProphet has built-in mechanisms to handle missing values in time series
data, reducing the need for manual preprocessing steps.

  * •

NeuralProphet incorporates a neural network model based on Long Short-Term
Memory (LSTM) units, enabling it to capture long-term dependencies and
nonlinear patterns in time series data.

For short forecasts, NeuralProphet claims to improve forecast accuracy
compared to Prophet by 55 to 92 percent [64].

####  4.4.3 N-BEATS

N-BEATS (Neural basis expansion analysis for interpretable time series
forecasting) is a state-of-the-art deep learning model for time series
forecasting. It was introduced by Oreshkin et al. in 2020. N-BEATS is designed
to capture complex temporal patterns in time series data and provide accurate
and interpretable forecasts [53].

N-BEATS works by decomposing a time series into a set of basic patterns called
”basis functions” which represent different types of patterns that can occur
in the data, such as trends, seasonality, or fluctuations. N-BEATS uses a
neural network architecture, composed of fully connected (FC) layers, to learn
the coefficients and weights associated with each basis function. These
coefficients determine the importance or contribution of each basis function
at different points in time. It starts with a large set of basis functions and
progressively selects the most influential ones, removing the less important
ones during training. N-BEATS offers interpretability by providing insights
into the underlying patterns. The learned basis functions can be visualised to
understand which patterns are driving the forecasts, helping users gain
insights into the data.

As an example, the N-BEATS tool allows for quick analysis of the features of
the dataset to determine the correlation between the features. Figure 9 below
is the result of a brief examination into the weather_features.csv dataset to
see how the features are connected. This information will be useful in
determining which features are the most important to explore within future
forecasting models.

Figure 9: Correlations between the features of the test dataset

###  4.5 Future Challenges and Recommendations

When analysing energy generation, it is important to recognise that wind and
solar energy each exhibit their own trends and characteristics. The extent to
which these trends are shared will need to be established, as well as how each
is influenced by factors such as temperature and general weather conditions.
Simple regression and decomposition techniques such as ARIMA or Prophet are
powerful tools that can be used to establish trends in historical data for
each energy source individually and as such will be a good starting point.
However, the correlation of the output from these energy sources with
attributes such as temperature cannot be ignored. Higher temperatures
generally enhance solar energy production due to increased sunlight intensity
and duration, whilst wind energy production is influenced heavily by
temperature gradients. Advanced techniques such as RNNs or LSTMs could be
considered to tackle these dependencies, improve accuracy, and capture long-
term patterns within the data. Issues which need to be explored within this
model include determining which features are the most important when making
forecasts. There are a great many atmospheric variables which can be
considered, and determining the most influential will require a greater
exploration of the data.

When exploring the forecasting of energy consumption, two suitable models
include Prophet and the deep learning model Neural Prophet. These models offer
several advantages that make them ideal for further exploration in energy
consumption forecasting. Both tools allow for easier examination of trends,
seasonalities, and the impact of holidays, all of which heavily affect user
behaviour and significantly impact energy consumption. A benefit to this
approach is that it allows for the exploration of non-intelligent time-series
decomposition techniques which from research have been shown to be
surprisingly effective in certain problem areas, whilst allowing for a
relatively easy transition to the deep-learning model extension. An
appropriate third alternative for exploration is an optimised LSTM model using
a large number of previous time-step data since the LSTM architecture will
allow for the capture of longer-term dependencies within time-series data. A
challenge to overcome when forecasting energy demand will be finding an
effective way to account for the nuanced cultural behaviours of the mini-grid
community. Maximising the reliability of the grid requires knowledge of trends
in behaviour, which might not become apparent until real data can be analysed.
Therefore, it is crucial to explore techniques that enable the selected models
to effectively handle unforeseen events and scenarios.

One of the best techniques to explore for handling the optimisation of
multiple objectives, as required for operation and management forecasting, is
fuzzy logic inference. Fuzzy logic is particularly suitable for this problem
due to the numerous features which will need to be to considered, as it can
effectively handle the uncertainty associated with each of these inputs using
a set of established fuzzy rules. To optimise hardware longevity, low cost,
low carbon emissions, and low risk, features such as time, battery state,
electricity buy and share price, load demand, renewable energy generation, and
weather forecasting will need to be considered. A simple fuzzy logic model
could account for all of these features whilst factoring in their associated
uncertainties in order to forecast optimal times to charge and discharge the
battery, perform maintenance, connect to the main grid to share energy, and
prepare for using the diesel generator. Exploring the hybrid ANFIS model would
be advantageous for leveraging the interpretability and linguistic reasoning
inherent in fuzzy logic, alongside the learning and adaptation capabilities
offered by neural networks. A core issue with this model will be determining
how to appropriately weigh the optimisation objectives based on a community’s
needs.

Another significant issue is the substantial delay in obtaining real data for
both training and evaluation purposes. This delay presents a notable challenge
as it raises uncertainties about the effectiveness of models trained solely on
test data when applied to real-world scenarios. To address this concern,
additional exploration of knowledge transfer techniques is essential. A
promising approach to consider is the integration of adapter layers into the
model architecture. Adapter layers can improve the models’ adaptability to
different data domains and enhance their performance on real data. This and
other knowledge transfer techniques must be explored for energy forecasting
projects.

Exploring the problem of selecting an appropriate forecast horizon is
essential as it requires striking a balance between capturing long-term trends
and mitigating excessive uncertainty. It will be important to thoroughly
examine the chosen models across different forecasting horizons and consider
data constraints to ensure their ability to accurately forecast within useful
time frames whilst minimising associated risks.

##  5 Conclusion

In conclusion, this survey has highlighted the significant potential of AI-
driven solutions to enhance the operation and sustainability of mini-grids in
rural communities. The integration of various forecasting techniques,
including statistical models, machine learning algorithms, and hybrid
approaches, has demonstrated the capability to address the inherent
unpredictability of renewable energy sources. Accurate forecasting of energy
supply and demand is crucial for optimising mini-grid operations and ensuring
reliable and cost-effective energy distribution.

The exploration of tools such as Prophet, NeuralProphet, and N-BEATS has shown
promising results in improving forecast accuracy and interpretability. These
tools provide valuable insights into trends, seasonality, and the impact of
external factors such as weather and holidays on energy consumption and
generation. The examination of public datasets has further emphasized the
importance of real-world data in developing and validating forecasting models.
However, challenges remain in adapting these models to specific local
conditions, accounting for cultural behaviours, and handling the delay in
obtaining real data for training and evaluation purposes.

Future research should focus on enhancing model adaptability through
techniques like knowledge transfer and the integration of adapter layers.
Additionally, the optimisation of forecasting horizons and the incorporation
of fuzzy logic inference for multi-objective optimisation are critical areas
for further exploration. By addressing these challenges, AI-driven mini-grid
solutions can significantly contribute to achieving sustainable and reliable
energy access for rural communities, thereby promoting economic development
and improving the quality of life.

Overall, this survey underscores the importance of continuous innovation and
adaptation in forecasting techniques to meet the evolving needs of mini-grid
systems. The collaborative efforts of researchers and industry partners are
essential in driving forward the development of robust and efficient mini-grid
solutions.

## Acknowledgements

The authors wish to express their heartfelt appreciation to Andrew McLeman for
his substantial contributions to this work. Andrew played a crucial role in
the literature review, significantly influencing the scope and depth of this
survey. Regrettably, he is no longer part of our team, and thus we are unable
to list him as a co-author. This project received funding from the UKRI Energy
Catalyst fund (Project Reference: 10040475), and we sincerely acknowledge
their generous support.

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