P.R.J. Campbell and K. Adamson (UK)
Energy Yield, Neural Network, Wind Power, Wind Speed Estimation.
Wind energy has emerged as the leading renewable energy generation method, currently producing a power yield equivalent to 20 GW, with an estimated projection of 40-60 GW by 2010. 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 park over a given period. This paper presents a wind power and subsequently an energy yield forecast tool which is based on a multi-layered perceptron. The tool produces energy yield forecasts which 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 Wind Turbine Generator (WTG) type and altitude to give an estimated power yield and expected uncertainty of the forecast (in terms of percentage capacity factor). Results indicate that by using a neural network approach the accuracy of the tool is sufficiently accurate to be considered as a feasible method for short to medium term (wind speed) power yield estimation for wind energy producers and utility operators.
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