Data Mining and Identification of Influencing Factors for Effective Load Forecasting of A Substation Using Machine Learning Regressors

Electric load forecasting (ELF), which is a fundamental step in the planning of the energy industry and is important for managing power systems and electric capacity scheduling, has drawn growing academic interest. Therefore, for the planning of energy generating capacity and for power system management, the accuracy of electric load forecasting is crucial. Recently, Load forecasting using Machine learning algorithms are the area of interest. Model designed based on featured data provides better result compared to that of based on raw data. Thus, data mining and feature extraction of raw data is the trending area for researcher. This paper deals with Data mining of historical data of Talapady substation through graphical approach and identifies the influencing factors such as season, month etc. Moreover, this work provides a brief idea to research scholars on how to analyze historical data and to identify the influencing factors through analysis. Proposed work validated using random forest regressor model.