Energy Demand Forecasting for Hybrid Microgrid Systems Using Machine Learning Models

Tahir Aja Zarma; Emmanuel Ali; Ahmadu Adamu Galadima; Tologon Karataev; Suleiman Usman Hussein; Adekunle Akanni Adeleke

doi:10.46604/peti.2024.14098

Authors

Tahir Aja Zarma Department of Electrical Electronics Engineering, Nile University of Nigeria, Abuja, Nigeria
Emmanuel Ali Department of Computer Engineering, Nile University of Nigeria, Abuja, Nigeria
Ahmadu Adamu Galadima Department of Electrical Electronics Engineering, Nile University of Nigeria, Abuja, Nigeria
Tologon Karataev Department of Electrical Electronics Engineering, Nile University of Nigeria, Abuja, Nigeria
Suleiman Usman Hussein Department of Electrical Electronics Engineering, Nile University of Nigeria, Abuja, Nigeria/ National Space Research and Development Agency, Abuja, Nigeria
Adekunle Akanni Adeleke Department of Mechanical Engineering, Nile University of Nigeria, Abuja, Nigeria

DOI:

https://doi.org/10.46604/peti.2024.14098

Keywords:

energy demand, forecasting, hybrid microgrid, machine learning

Abstract

This study aims to design energy demand forecasting models for energy management in hybrid microgrid systems using optimized machine learning techniques. By incorporating temperature, humidity, season, hour of the day, and irradiance, the complex relationship between these input parameters and the yield of photovoltaics, generator, and grid energy sources is examined. Five different machine learning models including linear regression, random forest (RF), support vector regression, artificial neural network, and extreme gradient boosting models are adopted in this study. Evaluation of model performance shows that the RF model is the best candidate for the dataset, with a mean-squared error of 0.2023, mean absolute error of 0.0831, root-mean-squared error of 0.4498, and R² score of 0.9992. Shapley additive explanations analysis identified key predictors such as hour, irradiation, and season while highlighting the negative impact of humidity and day of the week on energy demand.

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