Data Mining-Based Upscaling Approach for Regional Wind Power Forecasting: Regional Statistical Hybrid Wind Power Forecast Technique (RegionalSHWIP)

With the increasing need for the energy, the importance of renewable energy sources has also been increasing. In order to include the power produced by the wind into electricity grid in a controlled manner, power prediction has an important role. To produce a reliable wind power forecast, obtaining Wind Power Plants' (WPP) power generation data in real time and constructing the power forecast model with historical production values is a desirable action plan. However, this situation may not be applicable for all WPPs in the country due to difficulties in obtaining such data from WPP in real time. Therefore, there is a need for upscaling algorithm for generating the power forecast of such WPPs and producing a regional power forecast for a given region or the whole country. In this work it is aimed to construct an upscaling wind power forecast model to answer these needs. Many models in the literature propose techniques for the estimation of the entire zone rather than an offline plant. Offline plants are the plants such that their production data is not available in the system. In this work, we propose a method for generating power forecasting for offline plants, and for a region at the same time. The technique is based on firstly power forecasting for offline plants, and then upscaling to region by using forecasts for both online and offline wind power plants. The performance of the method is experimentally evaluated with baseline and previous techniques and it is shown to provide higher accuracy for power prediction.