Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning. In this context, machine learning data analytics models represent a valuable tool cope intrinsic complexity especially design future demand-side advanced services. The main novelty paper that combination of Recurrent Neural Network (RNN) Principal Component Analysis (PCA) techniques proposed improve capability hourly on an substation. A historical dataset measured loads related 33/11 kV MV substation considered India case study, order properly validate designed method. Based presented numerical results, approach proved itself accurately predict reduced dimensionality input data, thus minimizing overall computational effort.