A neural network based approach to wind energy yield forecasting

Abstract

It is commonly acknowledged that wind energy is the leading renewable energy generation method; currently producing a power yield equivalent to 35 GW, with an estimated projection of 40-60 GW by 2012. In order to successfully integrate wind energy with traditional generation supplies it is necessary to have the ability to accurately forecast the available yield of a wind installation during a period of time. In this paper we present a neural network based estimation tool which produces wind speed estimates for a given wind installation. These predications are subsequently used in industry standard calculations to produce an energy yield estimate for the wind installation over a given time period. The proposed approach produces forecasts that can be used for two main purposes; firstly, delivery of wind (energy) yield estimations and secondly to assess the suitability of a given location for development into a wind park site. The tool makes use of a Multi-layered Perceptron which has been trained with historical data to produce a set of predicted wind speed data for a given period. This data is then processed in conjunction with independent variables, including Turbine Generator type and altitude to give an estimated power yield and expected uncertainty of the forecast (in terms of percentage capacity factor). Our results indicate that by using such a neural network approach the accuracy of the tool is sufficiently accurate to for short to medium estimations and could prove a valuable tool for wind energy producers and utility operators. © 2010 Springer-Verlag Berlin Heidelberg.