A Novel Wind Power Forecast Model: Statistical Hybrid Wind Power Forecast Technique (SHWIP)


As the results of increasing population and growing technological activities, nonrenewable energy sources, which are the most energy providers, are diminishing daily. Due to the present issue, efforts on economical utilization of renewable energy sources have increased everywhere the world. Wind is one among the foremost important alternative energy resources. However, in comparison with different renewable energy sources, it's so variable that there is a want for estimating and coming up with of wind power generation. During this paper, a replacement statistical short-term (up to forty eight h) wind power forecast model, specifically statistical hybrid wind power forecast technique (SHWIP), is presented. Within the proposed model, weather events are clustered with respect to the most important weather forecast parameters. It also combines the facility forecasts obtained from three different numerical weather prediction (NWP) sources and produces a hybridized final forecast. The proposed model has been in operation at the 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 vital characteristics of the proposed model is the need for a lesser amount of historical information while constructing the mathematical model compared with the other statistical models like artificial neural networks (ANN) and support vector machine (SVM). To produce a reliable forecast, ANN and SVM want at least 1 year of historical knowledge; on the other hand, the proposed SHWIP methodology's results are applicable even beneath one month of training data, and this is an important feature for the forecast of the newly established wind power plants (WPPs).

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