Day ahead hourly load forecast of PJM electricity market and ISO New England market by using artificial neural network

Abstract

Short-term load forecasting is an essential instrument in power system planning, operation, and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. This paper discusses significant role of artificial intelligence (AI) in short-term load forecasting (STLF), that is, the day-ahead hourly forecast of the power system load over two weeks. Neural network fitting tool is used to compute the forecasted load. The data to be used in the model are hourly historical data of the temperature and electricity load. The models are trained on hourly data from the ISO New England market and PJM Electricity Market from 2007 to 2011 and tested on out-of-sample data from 2012. The simulation results have shown highly accurate day-ahead forecasts with very small error in load forecasting.