Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 9 , Issue 1 , PP: 53-71, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Marwa M. Eid 1 * , Anis Ben Ghorbal 2
Doi: https://doi.org/10.54216/JAIM.090106
This paper proposes to evaluate how different machine learning techniques can be used to predict daytime power generation based on the ”Daily Power Generation Data” dataset. As a result of six models, which contain Random Forest Regressor, Decision Tree Regressor, Nearest Neighbors, Linear Regression, MLP Regressor, and SVR, a clear understanding has been accomplished by assessing the performance using multiple metrics. First of all, the Random Forest Regressor turned out to be the best in terms of the Mean Squared Error (MSE) of 3.57×10−6, which was the lowest among the three ML models. The introduction of the paper highlights the role of precise planning of the power market and the consecutive sections describing the topic mathematically. The table below, with a total list of performance issues, explains why the Random Forest Regressor is the superior full-proof model using the lowest MSE, highest explained variance, and great resistance to outlying samples. The paper thus gave various useful approval criteria that, to a great extent, we can choose the best model out of them because the Random Forest Regressor was in a position to get the highest performance metrics.
Power Generation , Daily Power Generation , Machine Learning , Random Forest Regressor.
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