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




Linear regression models, Short-term forecasting, Electric power generation, Machine Learning Algorithms


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.


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Zjavka L, Snášel V. Short-term power load forecasting with ordinary differential equation substitutions of polynomial networks. Electric Power Systems Research. 2016 08;137:113-123.

Haida T, Muto S. Regression based peak load forecasting using a transformation technique. IEEE Transactions on Power Systems. 1994;9(4):1788-1794.

Hobbs B, Jitprapaikulsarn S, Konda S, Chankong V, Loparo K, Maratukulam D. Analysis of the value for unit commitment of improved load forecasts. IEEE Transactions on Power Systems. 1999;14(4):1342-1348.

Hippert H, Pedreira C, Souza R. Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems. 2001;16(1):44-55.

Swastanto B. Gaussian Process Regression for Long-Term Time Series Forecasting. Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology; 2016.

Singh S, Parmar KS, Kumar J, Makkhan SJS. Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19. Chaos, Solitons & Fractals. 2020 06;135:109866.

Zhang L, Lin J, Qiu R, Hu X, Zhang H, Chen Q, Tan H, Lin D, Wang J. Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model. Ecological Indicators. 2018 Dec;95:702-710.

Khan FM, Gupta R. ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India. Journal of Safety Science and Resilience. 2020 09;1(1):12-18.

Ma T, Antoniou C, Toledo T. Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast. Transportation Research Part C: Emerging Technologies. 2020 02;111:352-372.

Mohamed H, Negm A, Mohamed Z, Oliver C. S. Assessment of artificial neural network for bathymetry estimation using high resolution satellite imagery in shallow lakes: case study el burullus lake. International Water Technology Journal. 2015 December;5:352‑372.




How to Cite

Guenoupkati, A., Salami, A. A., Kodjo, M. K., & Napo, K. . (2021). Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B). TH Wildau Engineering and Natural Sciences Proceedings , 1.