Applying Climate Big-Data to Analysis of the Correlation between Regional Wind Speed and Wind Energy Generation

Abstract

In an era of growing concern over climate change, several utility companies originally supplied wholesale and retail power mainly made by burning coal, have started to consider and build the clean-energy power systems for resolving global warming problems. Wind power is nowadays regarded as one of the predominant alternative sources of clean energy. In this paper, we discuss our work on utilizing climate big-data associated with wind power, collected from several wind farms over four years, for exploring the correlation between regional wind speed and wind power. Once this huge amount of data are analyzed, it can be used to develop policies for siting wind-power facilities, designing smart charging algorithms, or evaluating the capacity of electrical distribution systems to meet the actual requirement of power load. Our work started with collecting related climate data for building data model to perform analytics work and experiments using Support Vector Regression (SVR) method. Also, we observed the correlations between other factors related to wind speed and wind energy from our empirical model. The preliminary experimental results demonstrate that our developed system framework is workable, allowing for detailed analysis of the important wind-power related factors on specific wind farm regions.