Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/3349 2022 2022 Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA Nima Nima Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03, Hradec Králové, Czech Republic Benyamin Abdollahzadeh 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" data set. 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, the Random Forest Regressor turned out to be the best in terms of the Mean Squared Error (MSE) of 3.57E-06, 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 we can largely choose the best model out of them because the Random Forest Regressor was able to get the highest performance metrics. 2024 2024 01 09 10.54216/JAIM.080201 https://www.americaspg.com/articleinfo/28/show/3349