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Calculation of the real daily and monthly mean working temperature of a photovoltaic plant

机译:计算光伏植物的实时和月平均工作温度的计算

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Accurately 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: T_(d10d) = 1.290 + 0.475 × T_(dmm) - 1.331 × T_(dmm)~(0.5) (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.
机译:准确预测光伏(PV)植物的生产对于其经济评估至关重要。环境温度是影响光伏厂产生的功率的主要环境因素。然而,大多数发展中国家的天气局只能提供日常或月平均环境温度,而不是PV植物附近的真实平均工作温度。此外,戈壁,沙漠和青藏高原的真实日常或每月平均工作温度和相应的日常或月平均温度之间的差异在一些目前的PV功率预测模型中引起一些错误。在本文中使用DataFit 9.0软件,为Beiluhe(青藏高原)和沙漠知识澳大利亚太阳能中心(Dkasc)(沙漠)数据创建了回归方程,与实时或月平均工作温度的差异和相应的平均温度有关(Y因子)以及日常或每月温差(X因子)。一个通用等式:T_(D10D)= 1.290 + 0.475×T_(DMM) - 1.331×T_(DMM)〜(0.5)(等式6)可以代表从BEILUHE和DKASC数据获得的所有回归方程。这些基本发现对于通过包括日常或月平均空气温度来提高现有光伏功率预测模型的准确性有价值。另一种研究结果将有助于PV工厂网站选择和评估。

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