Random Forest Regression with Lag Features for Power Output Prediction of Automatic Street Lighting System Based on Fresnel Solar Concentrator - Muhammad Vivaldi Vikzi Kohar; Evelina; Muhammad Nawawi (Universitas Lampung, 2026)
The variability of DC Street Lighting (PJU) output power based on Fresnel Solar Concentrator due to weather fluctuations presents a challenge in solar energy management. This study develops a prediction model using Random Forest Regression (RFR) with IoT sensor time-series lag features and BMKG data, utilizing 44 predictor variables from 405 records collected over 27 days. Hyperparameter optimization via 5-fold Grid Search Cross-Validation yielded n_estimators=200 and max_depth=15. Evaluation results achieved R²=0.9850 and MAPE=8.29%, satisfying all success criteria. Panel power (P_Panel) was the most dominant predictor with importance score 0.1197. The model successfully predicted output power for 7 days ahead with an average energy estimate of 250 Wh per day, proving that lag features integration into RFR is effective for proactive PJU energy management.