Statistical Wind Power Forecasting Models: Results for U.S. Wind Farms; Preprint

Electricity markets in the United States are evolving. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast makes it possible for grid operators to schedule the economically efficient generation to meet the demand of electrical customers. In the evolving markets, some form of auction is held for various forward markets, such as hour ahead or day ahead. This paper develops several statistical forecasting models that can be useful in hour-ahead markets that have a similar tariff. Although longer-term forecasting relies on numerical weather models, the statistical models used here focus on the short-term forecasts that can be useful in the hour-ahead markets. We investigate the extent to which time-series analysis can improve on simplistic persistence forecasts. This project applied a class of models known as autoregressive moving average (ARMA) models to both wind speed and wind power output.