000			01824nam a22002657a 4500
005			20251016094134.0
008			250909b ||||| |||| 00| 0 eng d
043			_aURS
245			_aPredicting of Solar Power Generation using Random Forest Regression _b/ Mentoy, Jane J.... [et al.].
260			_cNovember 2024
300			_a30 leaves : _billustrations ; _c28 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
502			_aThesis _bBachelor of Science in Mathematics _cUniversity of Rizal System-Morong _d2024
520			_aThis 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.
700			_aMentoy, Jane J. _eauthor
700			_aBandong, Charisse Mae B. _eauthor
700			_aEstremos, Danilo Jr. J. _eauthor
700			_aGonzales, Radnajelo S. _eauthor
700			_aOlinares, Gracelyn Y. _eauthor
700	1		_aDela Cruz, Shiela Marie F. _edegree supervisor
856			_3Online Request for Student Unpublished Works _uhttps://forms.gle/iwQgJ2wFRviEt3BA8
942			_2lcc _cT
999			_c86010 _d86008