Abstract
AbstractAccurately predicting the production of a photovoltaic (PV) plant is essential to its economic evaluation. Ambient temperature is a major environmental factor affecting how much power a PV plant generates. However, weather bureaus in most developing countries can only provide the daily or monthly mean ambient temperatures, rather than the real mean working temperature in the vicinity of the PV plant. Moreover, the difference between the real daily or monthly mean working temperature and the corresponding daily or monthly mean temperature is large in the Gobi, desert, and Qinghai‐Tibet Plateau, causing some errors in some present PV power predictive models. Using Datafit 9.0 software in this paper, regression equations were created for Beiluhe (Qinghai‐Tibet Plateau) and Desert Knowledge Australia Solar Centre (DKASC) (desert) data relating the difference of the real daily or monthly mean working temperature and the corresponding mean temperature (Y factor) as well as the daily or monthly temperature difference (X factor). One general equation: Td10d = 1.290 + 0.475 × Tdmm − 1.331 × Tdmm0.5 (Equation 6) could represent all of the regression equations obtained from the Beiluhe and DKASC data. These basic findings are valuable for improving accuracy of present PV power predictive model by including the daily or monthly mean air temperature. Another, the research result will be helpful for PV plant site selection and evaluation.
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