An artificial neural network model for electrical daily peak load forecasting with an adjustment for holidays

This paper presents an artificial neural network model for daily peak load forecasting. The input variables of the model have been selected based on their correlation coefficients. The model uses only three input variables. In addition, a new technique for selecting the training vectors is introduced. Moreover, the model presents a unique adjustment algorithm to compensate the negative impact of holidays' forecasts. Also, the model uses an adjustment technique for Sundays and Mondays forecasts as these two days showed higher error than the rest of the weekdays. Nevertheless, the model is simple, fast, and accurate. The mean percent relative error of the model over a period of one year is 2.066% including holidays.