A stochastic load model for an electricity market

This paper presents a stochastic load model that uses a regression equation coupled with a time series model. The model is simple but without compromising accuracy. A 24-h set of regression equations incorporates the hourly temperature variations. Weekly seasonality is handled by providing weekday and weekend non-linear regression equations. The Levenberg-Marquard method is used because of its superiority over the widely used Gauss-Newton and steepest descent methods in estimating model parameters and to avoid "slow down" in the search process, respectively. A residual discrete time series is determined by using the autoregressive integrated moving average (ARIMA) model. Test results using PJM-market load data indicate the effectiveness of the proposed model to predict the daily electricity load. © 2005 Elsevier B.V. All rights reserved.