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.