TY  - BOOK
AU  - Mentoy, Jane J.
AU  - Bandong, Charisse Mae B.
AU  - Estremos, Danilo Jr. J.
AU  - Gonzales, Radnajelo S.
AU  - Olinares, Gracelyn Y.
AU  - Dela Cruz,Shiela Marie F.
TI  - Predicting of Solar Power Generation using Random Forest Regression: / Mentoy, Jane J.... [et al.]
PY  - 2024///
N1  - Thesis
N2  - 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
UR  - https://forms.gle/iwQgJ2wFRviEt3BA8
ER  -