According to numbers and charts reported recently in the global renewable status and 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 by using suitable optimization techniques. Therefore, this paper introduces a novel technique based on statistical and datamining analyses to derive the key parameters for optimizing HRES in two main steps. Firstly, it conducts a comprehensive review of hundreds of the most recent research papers on HRES. Secondly, it runs statistical calculations and data mining analysis on the big data that are collected by the first step. The outcomes of this step are represented in form of statistical results and frequent patterns rulesets. The statistical results have been validated with the frequent pattern rulesets to verify the correctness of the overall results. As a result, the paper produces two databases. The first one which is obtained from the comprehensive review, introduces a great guide for researchers to determine the best techniques for optimizing HRES systems. The second database that is resulted from statistical calculations and data mining analysis, determines the key parameters for optimizing HRES and hence developing the renewable energy systems in order to increase the system reliability and reduce the cost as well as environmental impacts.