Energy Demand Forecasting for Hybrid Microgrid Systems Using Machine Learning Models - Abstract

Aug 3, 2026

Uploaded by Theodore

Energy demand forecasting models are developed to support energy management in hybrid microgrid systems through optimized machine learning. The study models the relationship between meteorological and temporal inputs—temperature, humidity, season, hour of day, and irradiance—and the energy yield from photovoltaic, generator, and grid sources. Five regression models are tested: linear regression, random forest, support vector regression, artificial neural network, and extreme gradient boosting. Random forest delivers the strongest performance, while SHAP analysis highlights hour, irradiation, and season as key positive predictors and shows humidity and day of the week reduce demand.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Abstract

Introduction

Energy Demand Forecasting for Hybrid Microgrid Systems Using Machine Learning Models Tahir Aja Zarma 1,*, Emmanuel Ali2, Ahmadu Adamu Galadima 1, Tologon Karataev 1, Suleiman Usman Hussein 1,3, Adekunle Akanni Adeleke 4 1Department of Electrical Electronics Engineering, Nile University of Nigeria, Abuja , Nigeria 2Department of Computer Engineering, Nile University of Nigeria, Abuja , Nigeria 3National Space Research and Development Agency, Abuja, Nigeria 4Department of Mechanical Engineering, Nile University of Nigeria, Abuja, Nigeria Received 05 August 2024; received in revised form 07 October 2024; accepted 11 October 2024 DOI: [https://doi.org/10.46604/peti.2024.14098](https://doi.org/10.46604/peti.2024.14098) 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 R²score 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. Keywords: energy demand, forecasting, hybrid microgrid, machine learning 1. Introduction To properly size their energy supply systems, institutions need precise energy demand forecasts. Global electric power systems depend on load forecasts for energy trading, operational strategies, and planning. Deregulation and competitive markets have transformed traditional sectors, and the Kyoto Protocol mandates both a reduction in carbon emissions and an increase in renewable energy integration, which are crucial for energy sustainability [1] . Renewable sources are intermittent and affected by weather, while electricity demand varies with weather, population distribution, and lifestyle patterns, necessitating accurate forecast models for proper energy system sizing [1, 2] . Integrating renewable energy into power systems adds complexity, and creates challenges in maintaining energy balance [3] . Therefore, to balance supply and demand, reduce operation costs, and increase energy system reliability, energy demand forecasting is crucial. In hybrid microgrid systems, which integrate non-renewable energy with renewable energy sources, this task becomes even more complex [4] . Because of their unpredictability and uncertainty, renewable energy sources require sophisticated forecasting models capable of handling large datasets and identifying intricate patterns in energy usage. Energy demand forecasting has shown significant potential with machine learning models. Linear regression (LR) is effective for predicting demand when variable relationships are linear [5], while random forest (RF) offers excellent capability in handling large datasets with multiple features for regression [6] . * Corresponding author. E-mail address: [tahiraja@nileuniversity.edu.ng](tahiraja@nileuniversity.edu.ng) Support vector regression (SVR), on the other hand, is noted for its robustness against overfitting, particularly with variable data [7, 8] . Meanwhile, artificial neural network (ANN) models benefit from enhanced predictive accuracy and convergence when optimized using the ADAM optimizer [9, 10] . Recent research highlighted the use of machine learning (ML) models for forecasting energy consumption, with SunYoun Shin et al. [11, 12] s

FAQs

Which machine learning models are used for energy demand forecasting in hybrid microgrids?

The study evaluates linear regression, random forest, support vector regression, artificial neural network, and extreme gradient boosting models.

What input factors are included to predict energy demand?

Inputs include temperature, humidity, season, hour of day, and irradiance, linked to photovoltaic, generator, and grid energy yields.

Why is the random forest model considered the best performer?

Model evaluation shows random forest achieves the lowest errors (MSE 0.2023, MAE 0.0831, RMSE 0.4498) and a very high R² of 0.9992.

Aug 3, 2026

Uploaded by Theodore