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Methodology of K?ppen-Geiger-Photovoltaic climate classification and implications to worldwide mapping of PV system performance
摘要： Photovoltaic (PV) already proves but even more promises to be massively deployed worldwide. To evaluate the performance of PV systems globally and assess risk due to different climate conditions, we propose a methodology for the global K?ppen-Geiger-Photovoltaic (KGPV) climate classification that divides the globe into 12 zones with regard to the temperature, precipitation and irradiation, and standardizes the evaluations of the performance in regions with similar climatic conditions. Additionally, we present implications of KGPV to simulated PV performance using monthly data, for current and future operation of PV systems worldwide including climate change scenarios. A set of electrical and thermal performance indicators of crystalline silicon PV modules in different KGPV zones is analyzed and their evolution over time due to climate changes caused by high greenhouse gas emissions discussed. Results show that the KGPV scheme proves to be a convenient methodology to relate the KGPV climate zones with PV performance.
关键词： Photovoltaic,PV systems,Performance,Climate change,Climate zones
更新于2025-09-12 10:27:22
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Comparison of Artificial Intelligence and Physical Models for Forecasting Photosynthetically-Active Radiation
摘要： Different kinds of radiative transfer models, including a relative sunshine-based model (BBM), a physical-based model for tropical environment (PBM), an efficient physical-based model (EPP), a look-up-table-based model (LUT), and six artificial intelligence models (AI) were introduced for modeling the daily photosynthetically-active radiation (PAR, solar radiation at 400–700 nm), using ground observations at twenty-nine stations, in different climatic zones and terrain features, over mainland China. The climate and terrain effects on the PAR estimates from the different PAR models have been quantitatively analyzed. The results showed that the Genetic model had overwhelmingly higher accuracy than the other models, with the lowest root mean square error (RMSE = 0.5 MJ m?2day?1), lowest mean absolute bias error (MAE = 0.326 MJ m?2day?1), and highest correlation coefficient (R = 0.972), respectively. The spatial–temporal variations of the annual mean PAR (APAR), in the different climate zones and terrains over mainland China, were further investigated, using the Genetic model; the PAR values in China were generally higher in summer than those in the other seasons. The Qinghai Tibetan Plateau had always been the area with the highest APAR (8.668 MJ m?2day?1), and the Sichuan Basin had always been the area with lowest APAR (4.733 MJ m?2day?1). The PAR datasets generated by the Genetic model, in this study, could be used in numerous PAR applications, with high accuracy.
关键词： photosynthetically-active radiation,climate zones,physical models,artificial neural network,terrain features
更新于2025-09-09 09:28:46