A factor analysis and self-organizing map based evaluation approach for the renewable energy heating potentials at county level: A case study in China. (September 2022) - Zheng, Xuejing; Yang, Xueqing; Miao, Hongfei; Liu, Huzhen; Yu, Yanzhe; Wang, Yaran; Zhang, Huan; You, Shijun
Abstract: Reflecting on the sharp contradictions between energy and environmental concerns, nowadays, a critical theme is to strengthen the role of renewables for heating. The majority of current renewable energy (RE) studies are for power generation, research at the macro level, with subjective evaluation methods. Aimed at filling the niche, this paper proposes an integrated approach involving the investigation of renewable energy (solar, geothermal and biomass) heating potentials, the evaluation of suitability at the county level, and the guidance of targeted policies. Accordingly, the factor analysis, an objective statistical analysis method, is employed to concentrate information and score appropriateness based on the county development level, population quality, government support and renewable resource potentials. Subsequently, the comprehensive scores for counties are categorized into four classes by self-organizing maps. In this study, a novel approach for evaluating the RE heating potentials and identifying the constraints of RE development is proposed. The heating demand and the potentials of renewables are determined on a detailed and precise scale at the county level, and the heating supply-demand is mapped with symbol sizes. Finally, specific recommendations for renewables energy development are put forward from a local planning perspective. The application of this systematic approach, which can be broadly exported and applied to other regions, will promote a strong commitment to renewable energy and significantly accelerate the transition to a clean energy supply. Highlights: Evaluate the suitability of renewable energy heating at a detailed scale. Integrated renewable energy suitability scores are derived by the factor analysis. The development indicators are categorized into four classes by the SOM method. Regional development characteristics are identified by the extracted factors. Targeted renewable energy development policies are proposed.