Mining optimum models of generating solar power based on big data analysis
Solar Energy, (October 2017)DOI: 10.1016/j.solener.2017.06.035
Abstract
Finding the optimum models of generating solar power on real big data. Collected data are with diversity such as designs, produced power and environments. Three methods based on different user requests are proposed to reach the goals. The system is implemented and the conducted experiments are comprehensive. Majority voting has good recommendation accuracy. Recently in exploiting green energy, solar power generation is a must-be trend and approach, especially for the countries with nature resource shortage. However, how to build solar power plants with the best power generation efficiency in limited spaces is always a crucial issue. In this paper, the approach of finding the optimum models of generating solar power is proposed to build solar power plants for different environments in Taiwan. First, we collect all the data from existing solar power farms, including (1) design methods of power generation, (2) actual power generation, and (3) surrounding environments. Then, after a series of preprocessing steps and system analysis on them, the optimal models of generating solar power could be mined out. Finally, in the experiments, we evaluate the system from five aspects regarding to input and output parameters. As a result, we observe that using the majority voting strategy improves the system accuracy and helps engineers build solar power plants with the maximum power generation.