Optimization of energy usage decisions through predictive modeling of consumption patterns
Department of Software Engineering, Faculty of Natural and Applied Science, Veritas University Abuja, FCT, Abuja, Nigeria.
Research Article
Open Access Research Journal of Science and Technology, 2025, 15(01), 001–011.
Article DOI: 10.53022/oarjst.2025.15.1.0108
Publication history:
Received on 09 July 2025; revised on 29 August; accepted on 01 September 2025
Abstract:
Accurate energy consumption prediction is a critical resource allocation, reducing operational costs, and supporting sustainable energy management. This study presents a machine learning-based energy consumption prediction system, comparing the performance of Linear Regression, Ridge Regression, Lasso Regression, and Decision tree models. Using a historical dataset of consumer energy usage, key features were extracted and preprocessed to enhance predictive accuracy. Model evaluation employed Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination metrics. Results indicate that while Linear Regression offered competitive baseline performance, Ridge and Lasso Regressions achieved improved stability against multicollinearity, and the Decision Tree Model delivered the highest predictive accuracy, albeit with potential overfitting risks. The findings contribute to developing scalable, data-driven forecasting frameworks applicable to smart grids, policy planning, and demand-side management.
Keywords:
Energy forecasting; Machine learning; Ridge regression; Lasso regression; Decision tree; RSME
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0