Performance Analysis of Daily Global Solar Radiation Models in Peru by Regression Analysis

Abstract

Solar radiation (Rs) is one of the main parameters controlling the energy balance at the Earth’s surface and plays a major role in evapotranspiration and plant growth, snow melting, and environmental studies. This work aimed at evaluating the performance of seven empirical models in estimating daily solar radiation over 1990–2004 (calibration) and 2004–2010 (validation) at 13 Peruvian meteorological stations. With the same variables used in empirical models (temperature) as well as two other parameters, namely precipitation and relative humidity, new models were developed by multiple linear regression analysis (proposed models). In calibration of empirical models with the same variables, the lowest estimation errors were 227.1 and 236.3 J·cm−2·day−1 at Tacna and Puno stations, and the highest errors were 3958.4 and 3005.7 at San Ramon and Junin stations, respectively. The poorest-performing empirical models greatly overestimated Rs at most stations. The best performance of a proposed model (in terms of percentage of error reduction) was 73% compared to the average of all empirical models and 93% relative to the poorest result of empirical models, both at San Ramon station. According to root mean square errors (RMSEs) of proposed models, the worst and the best results are achieved at San Martin station (RMSE = 508.8 J·cm−2·day−1) and Tacna station (RMSE = 223.2 J·cm−2·day−1), respectively.