A Vector Autoregression Weather Model for Electricity Supply and Demand Modeling

Weather forecasting is crucial to both the demand and supply sides of electricity markets. Temperature has a great effect on energy demand. Moreover, solar and wind are very promising renewable energy sources. In this paper, a large vector autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States. We estimate the VAR model with 16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance. Our results show that the proposed time series approach is appropriate for short-term forecasting of solar radiation, temperature, and wind speed.