Enhancing Demand Response Forecasting Through Random Forest Regression: A Case Study Analysis

Demand response (DR) plays a crucial role in optimizing energy consumption and ensuring grid reliability. This study investigates the efficacy of Random Forest Regression (RFR) as a predictive tool for DR forecasting, focusing on its performance within the context of a specific dataset. Utilizing data on residential energy consumption patterns, we conducted a comparative analysis of RFR against alternative regression algorithms. The results reveal promising outcomes, suggesting that RFR outperforms other models in terms of predictive accuracy and generalizability, particularly within our dataset. Furthermore, the study explores parameter optimization strategies and assesses the impact of varying 'n_estimators' on model performance. The findings underscore the potential of RFR in forecasting load dynamics, thereby facilitating informed decision-making in DR program planning and implementation. This research contributes valuable insights into enhancing DR forecasting methodologies, paving the way for more effective and sustainable energy management practices.