Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B)

Abstract

Time series forecasting in the energy sector is important to power utilities for decision making to ensure the sustainability and quality of electricity supply, and the stability of the power grid. Unfortunately, the presence of certain exogenous factors such as weather conditions, electricity price complicate the task using linear regression models that are becoming unsuitable. The search for a robust predictor would be an invaluable asset for electricity companies. To overcome this difficulty, Artificial Intelligence differs from these prediction methods through the Machine Learning algorithms which have been performing over the last decades in predicting time series on several levels. This work proposes the deployment of three univariate Machine Learning models: Support Vector Regression, Multi-Layer Perceptron, and the Long Short-Term Memory Recurrent Neural Network to predict the electricity production of Benin Electricity Community. In order to validate the performance of these different methods, against the Autoregressive Integrated Mobile Average and Multiple Regression model, performance metrics were used. Overall, the results show that the Machine Learning models outperform the linear regression methods. Consequently, Machine Learning methods offer a perspective for short-term electric power generation forecasting of Benin Electricity Community sources.

Machine Intelligence in Africa: a survey

“…The work in [424] detects banana plants and their major diseases through aerial images and machine learning methods, focusing on a case study in DR Congo and the Republic of Benin. The work in [425] involves short-term electricity generation forecasting using machine learning algorithms, with a case study of the Benin Electricity Community (CEB). The work in [426] utilizes mathematical modeling and machine learning for public health decision-making, with a focus on the case of breast cancer in Benin.…”

Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches

“…The integrated part (I) of the model (d) includes the model terms that incorporate the amount of differentiation to be applied to the time series. The moving average part of the model (q) allows us to define the error of our model as a linear combination of error values observed at previous times [8]. The ARIMA (p,d,q) model using the lag polynomial L is illustrated by Equation ( 10) [8].…”

“…The moving average part of the model (q) allows us to define the error of our model as a linear combination of error values observed at previous times [8]. The ARIMA (p,d,q) model using the lag polynomial L is illustrated by Equation ( 10) [8].…”

“…The Akaike information criterion (AIC) is a commonly used measure for selecting the parameters of an ARIMA model. The AIC criterion is based on the maximization of the likelihood function of the model and takes into account the complexity of the model by adding a penalty for the number of estimated parameters in the model [8,22]. The Akaike criterion is defined as follows: AIC = 2k − 2 ln(Γ) (11) where k is the number of estimated parameters in the model and Γ is the maximized likelihood function for the model.…”

“…In Togo, Guenoukpati et al used three machine learning approaches, namely, Support Vector Regression (SVR), the multilayer perceptron (MLP) approach, and a long short-term memory (LSTM) recurrent neural network to predict the electricity production of the Benin Electricity Community. The results of their work argue that machine learning methods offer better results than linear regression methods and are better suited for short-term predictions [8]. Yalcinoz et al also used MLP to predict medium0 and short-term electrical load in Turkey [9].…”