Abstract
In this paper, measured data of solar radiation was applied to develop forty-three (43) empirical models for estimation of monthly average diffuse solar radiation using clearness index, sunshine duration and a combination of them as predictors. The data covered a period of two years from May 2015 to April 2017 and was measured at Mehran University of Engineering and Technology, Hyderabad, Pakistan. Through a comprehensive statistical performance analysis, 43 dimensional models developed were tested for constructing the most accurate regression model to predict the monthly mean daily diffuse solar radiation in Hyderabad, Pakistan. On the whole, the model 42 – a hybrid of sunshine duration and clearness index predictors of diffuse fraction outperformed the remaining models proposed in this study. The best model (model 42) was then compared with 5 models and 5 measured data of diffuse solar radiation available in the literature and the NASA database by applying statistical indicators such as MBE, MPE, RMSE, RRMSE, R 2 and GPI. Through the analysis, the hybrid of sunshine duration and clearness index predictors of diffuse fraction model (model 42) was selected as the most appropriate model. The study concluded that the proposed hybrid model can serve as a baseline for the design of photovoltaic systems and estimate the monthly mean daily diffuse solar radiation on the horizontal surface for Hyderabad, Pakistan and other locations with similar local climate conditions. Citation: Nwokolo, S.C. and Otse, C.Q. (2019). Impact of Sunshine Duration and Clearness Index on Diffuse Solar Radiation Estimation in Mountainous Climate. Trends in Renewable Energy, 5, 307-332. DOI: 10.17737/tre.2019.5.3.00107
Highlights
Since the beginning of the 19th century, the exploitation of conventional fuels is increasingly moving towards the development of industrialization and modern life style
The main objective of this study was to estimate forty-three models employed for estimating diffuse solar radiation using sunshine duration, clearness index and both of the predictors, obtain the best performing model using statistical indicators (such as mean bias error (MBE), mean percentage error (MPE), root mean square error (RRMSE), coefficient of determination (R2) and global performance indicator (GPI)), and compare the selected best models with five models developed from the literature and five ground measured diffuse solar radiation in the literature together with satellite data obtained from NASA database for estimating diffuse solar radiation in Hyderabad, Pakistan
The results of the measured data in the study site were compared with the following: (1) the developed 43 models in this study, (2) five measured data obtained from the literature together with the observed satellite data obtained from the NASA database, and (3) five models obtained from the literature and best performing model as presented in Figs. 2 - 5, and their corresponding estimation statistical indicators are presented in Tables 6 – 7
Summary
Since the beginning of the 19th century, the exploitation of conventional fuels is increasingly moving towards the development of industrialization and modern life style. It has resulted in various health hazards, environmental pollution, disruption of ecosystems such as crop and animal diversity, increased global warming and many more factors which drive the earth towards a dark future. The multinational Arctic Climate Impact Assessment reported that the Arctic is sensitive to atmospheric pollution and greenhouse effect. According to the IPCC 2007 report, based on the work of about 2,500 scientists in more than 130 countries, humans have caused all or most of the current planetary warming often called anthropogenic climate change. Nowadays change has taken place over the past 100 years or less
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