Multi-step-ahead, Short-Term Prediction of Wind Speed Using a Fusion Approach

Abstract

Wind power generation is a green solution to power generation that is receiving increasing interest worldwide. Wind speed forecasting is critical for this technology to succeed and remains today as a challenge to the research community. This paper presents a neural network fusion approach to multi-step-ahead, short-term forecasting of wind speed time-series. Wind speed forecasts are generated using a bank of neural networks that combine predictions from three different forecasters. The wind speed forecasters include a naive model; a physical model and a custom designed artificial neural network model. Data used in the experiments are telemetric measurements of weather variables from wind farms in Eastern Canada, covering the period from November 2011 to October 2012. Our results show that the combination of three different forecasters leads to substantial performance improvements over recommended reference models.