论文部分内容阅读
Increasing use of photovoltaic (PV) power system necessitates models capable of predicting solar radiation data;that will support designing of optimum sizes of distributed and large-scale PV power systems for an area of interest.However,many existing models require meteorological data as inputs while predicting solar radiation.The applicability of these models is limited particularly in places where observed meteorological data is rare (i.e.,in rural remote places).Here we proposed a model (which requires minimum amount of input i.e.,latitude and longitude of a location) for predicting global solar radiations in horizontal surface.The model results were tested in two locations of the United States representing clear and cloudy skies:1)Austin,Texas (a representative of clear sky condition);and 2) and Seattle,Washington(a representative of cloudy sky condition).Predicted global solar radiation values of each hour (8760 hrs) and monthly average daily were compared with measured values.The model performance was evaluated using model performance indicators such as mean absolute error (MAE),root mean square error (RMSE),and coefficient of determination (R2).The results of the model predictions are well matched with measured global solar radiation of each location.The approach proposed here will be useful in predicting solar radiation data important for designing PV system for the regions,where limited availability of meteorological data inhibits solar radiation predictions.