A Neural Network Approach to Multi-step-ahead, Short-Term Wind Speed Forecasting

Abstract

This paper presents a novel neural network-based approach to short-term, multi-step-ahead wind speed forecasting. The methodology combines predictions from a set of feed forward neural networks whose inputs comprehend a set of 11 explanatory variables related to past averages of wind speed, direction, temperature and time of the day, and their outputs represent estimates of specific wind speed averages. Forecast horizons range from 30 minutes up to 6:30 hours ahead with 30 minutes time steps. Final forecasts at specific horizons are combinations of corresponding neural network predictions. Data used in the experiments are telemetric measurements of weather variables from five wind farms in eastern Canada, covering the period from November 2011 to April 2013. Results show that the methodology is effective and outperforms established reference models particularly at longer horizons. The method performed consistently across sites leading up to more than 60% improvement over persistence and 50 % over a more realistic MA-based reference.