Day Ahead Load Forecasting using Random Forest method with meteorological variables

Abstract

This paper focuses on short-term load forecasting for the day ahead using an Ensemble learning-based Random Forest method. The study uses real-time hourly load data and meteorological data from Bengaluru city, Karnataka, India, to predict the load. The inputs considered for the load forecasting are load profile data, dry bulb temperature, dew point temperature, and humidity data for 31 days from January 1, 2021, to January 31, 2021. The results obtained from the Random Forest model are compared with those obtained from the Ensemble learning-based Bootstrap Aggregation model to evaluate the effectiveness of the proposed method. The study uses statistical parameters such as Maximum Absolute Percent Error (MAPE), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE) to analyze the predicted load. The findings indicate that the proposed Random Forest model yields better results, with a Mean Absolute Percentage Error (MAPE) of 2.75% compared to the other Ensemble learning-based Bootstrap Aggregation method.