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Statistical Hybrid Wind Power Forecast Technique (SHWIP)

As the result of increasing population and growing technological activities, non-renewable energy sources, which are the main energy providers, are diminishing day by day. Due to this factor, efforts on efficient utilization of renewable energy sources have increased all over the world. Wind is one of the most significant alternative energy resources. However, in comparison to other renewable energy sources it is so variable that there is a need for estimating and planning of wind power generation. In this paper, a new statistical short term (up to 48 hour) wind power forecast model, namely Statistical Hybrid Wind Power Forecast Technique (SHWIP) is presented. In the proposed model, weather events are clustered with respect to the most important weather forecast parameters.

It also combines the power forecasts obtained from three different Numerical Weather Prediction (NWP) sources and produces a hybridized final forecast. The proposed model has been in operation at Wind Power Monitoring and Forecast System for Turkey (RITM) and the results of the new model are compared with well-known statistical models and physical models in the literature. The most important characteristics of the proposed model is the need for less amount of historical data while constructing the mathematical modelling compared to the other statistical models such as Artificial NeuralNetworks (ANN) and Support Vector Machine (SVM). To produce a reliable forecast, ANN and SVM need at least one year of historical data, on the other hand proposed SHWIP method’s results are applicable even under one month of training data and this is an important feature for the forecast of the newly established Wind Power Plants.

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