Estimation methods of global solar radiation, cell temperature and solar power forecasting: A review and case study in Eskisehir


AYVAZOĞLUYÜKSEL Ö., BAŞARAN FİLİK Ü.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, cilt.91, ss.639-653, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 91
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.rser.2018.03.084
  • Dergi Adı: RENEWABLE & SUSTAINABLE ENERGY REVIEWS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.639-653
  • Anahtar Kelimeler: Solar radiation, Cell temperature, Power generation, TILTED SURFACES, PHOTOVOLTAIC MODULES, INCLINED SURFACES, DIFFERENT MODELS, OPERATING TEMPERATURE, IRRADIANCE, PREDICTION, PERFORMANCE, DIFFUSE, INSOLATION
  • Anadolu Üniversitesi Adresli: Evet

Özet

Solar energy is the most important energy resource that has become an efficient solution to the world's energy challenges. Accurate knowledge of global solar radiation and cell temperature are required for solar photovoltaic power forecasting. In this paper, a comprehensive literature review of methods used for estimation of global solar radiation, cell temperature and solar power generation forecasting are presented. In addition, a comparative analysis is presented using the actual data, which is collected from a home placed in Anadolu University Iki Eylul Campus in Eskisehir as a comprehensive case study. Within the scope of this analysis, hourly global solar radiation values on horizontal surface are estimated from the measured daily global solar radiation values by using eleven different models. By using the selected model, estimated hourly global solar radiation values for horizontal surface are converted to values for inclined surface. Cell temperature of the photovoltaic modules is estimated with various known models in the literature. Based on the estimated cell temperature and global solar radiation values on inclined surface, power generation values of the on-grid and off-grid systems are forecasted. Therefore, based on the statistical analysis, the most accurate models are recommended to be carried out in any location that has similar climatic conditions with the considered city.