The hourly load forecasting based on linear Gaussian state space model

Abstract

In this paper, the linear gaussian state space model is used to forecast the hourly electricity load. Since the weather variables have significant impacts on electricity demand, thus in our forecasting model, the weather variables are considered as explanatory variables and added to the state space model. The variance parameters of the linear gaussian state space are estimated by the Markov chain Monte Carlo method. Given the estimated parameters, the linear gaussian state space is used to forecast the electricity load on two hours SAM and 14PM respectively. The result shows that this model has higher forecasting precision than the one to four days ahead forecasting, and the state space model estimated by Gibbs sampling algorithm has better performance than the model based on the MH algorithm.