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Enhancing the Optimization of Hybrid Renewable Energy Systems by Using Statistical Calculations and Data Mining Analysis

According to numbers and charts reported recently in the global renewable status and the international energy agency, Renewable energy sources (RES) surpassed the natural gas and occupied the second position as a source of electricity. However, the intermittent nature of RES, causes a big problem of trifold dimensions: System cost, environmental impacts, and system reliability. Studies found that, using diverse RES combined in hybrid renewable energy systems (HRES) can handle these issues efficiently but only when utilizing good optimization techniques. Consequently, this paper derives the most suitable optimization techniques of HRES in two steps. Firstly, it conducts a comprehensive review of hundreds of the most recent research papers on HERS. Secondly , it runs statistical calculations and data mining analysis on the big data collected in the first step. The outcomes of these calculations and analyses are represented in form of statistical results and frequent patterns rulesets. Results proved that the statistical results go a long with the rulesets and they give typical results. Thereby, they validated each other and verified the correctness of the overall results. As a result, the paper produces two great databases. The first one can be considered as a guide for researchers to determine the best techniques for optimizing HERS systems. The second database of statistical results and frequent pattern rulesets can help researchers immensely in predicting the most suitable optimization techniques and algorithms for designing and developing HERS systems.