Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model

Abstract

This paper presents a novel time-varying weather and load model for solving the short-term electric load-forecasting problem. The model utilizes moving window of current values of weather data as well as recent past history of load and weather data. The load forecasting is based on state space and Kalman filter approach. Time-varying state space model is used to model the load demand on hourly basis. Kalman filter is used recursively to estimate the optimal load forecast parameters for each hour of the day. The results indicate that the new forecasting model produces robust and accurate load forecasts compared to other approaches. Better results are obtained compared to other techniques published earlier in the literature.