Short-term load forecasting of Ontario Electricity Market by considering the effect of temperature

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. A new artificial neural network (ANN) has been designed to compute the forecasted load. The data used in the modeling of ANN are hourly historical data of the temperature and electricity load. The ANN model is trained on hourly data from Ontario Electricity Market from 2007 to 2011 and tested on out-of-sample data from 2012. Simulation results obtained have shown that day-ahead hourly forecasts of load using proposed ANN is very accurate with very less error. However load forecast considering the effect of temperature is better than without taking it as input parameter.