Wind Power Forecasting

Abstract

Abstract Accurate short-term wind power forecast is very important for reliable and efficient operation of power systems with high wind power penetration. There are many conventional and artificial intelligence methods that have been developed to achieve accurate wind power forecasting. Time-series based algorithms are known to be simple, robust, and have been used in the past for forecasting with some level of success. Recently some researchers have advocated for artificial-intelligence based methods such as Artificial Neural Networks (ANNs), Fuzzy Logic, etc., for forecasting because of their flexibility. This paper presents a comparison of conventional and two artificial intelligence methods for wind power forecasting. The conventional method discussed in this paper is the Autoregressive Moving Average (ARMA) which is one of the most robust and simple time-series methods. The artificial intelligence methods are Artificial Neural Networks (ANNs) and Adaptive Neuro-fuzzy Inference Systems (ANFIS). Simulation results for very-short-term and short-term forecasting show that ANNs and ANFIS are suitable for the very-short-term (10 minutes ahead) wind speed and power forecasting, and the ARMA is suitable for the short-term (1 hour ahead) wind speed and power forecasting.