Thesis Bachelor of Science in Mathematics University of Rizal System-Morong 2024

This research explores predicting solar power consumption at Kasarinlan Ecopark in Baras, Rizal, using Random Forest Regression. Solar energy is increasingly used as a sustainable alternative to fossil fuels due to its environmental benefits and low maintenance requirements. However, accurately predicting the power output from solar panels remains a challenge because the voltage generated can vary greatly and is difficult to estimate. This study addresses this issue by applying four supervised machine-learning techniques to predict solar power consumption. The results show that Random Forest Regression provides the most accurate predictions with minimal errors. The findings highlight the effectiveness of using Random Forest for solar energy forecasting, helping to improve the efficient use of solar power in grid systems.