Performance evaluation of independent global solar radiation estimation models for different climatic zones: A case study

ABSTRACT

Rapid technological advancement in solar applications requires accurately determined daily global solar radiation (DGSR). The independent models to predict DGSR are super cited as they do not require any other climatological or geographical parameter. In this study, nine day-of-the-year based (DYB) independent models are developed to estimate daily global solar radiation in Pakistan. The meteorological data from the year 2000–2017 is used while covering thirty locations from five climatic zones of Pakistan. The models’ performance is statistically assessed in terms of normalized mean bias error (nMBE) and normalized root mean square error (nRMSE). The results indicate that the two Gaussian function model estimates DGSR with a satisfactory performance at most stations in different climatic zones. On average, the correlation coefficient and index of agreement between day-of-the-year and DGSR are 0.934 and 0.956, respectively. The two Gaussian function model is then further compared with four existing best claimed models found in literature. Results show that the nMBE and nRMSE ranges from 0.87 to 3.00 and 0.95 to 3.21, respectively. Thus, it shows that the existing models overestimate the DGSR and are not applicable for long-term planning. In comparison, for the present study, nMBE and nRMSE take values between −0.05 to 0.17 and 0.09 to 0.24, respectively. Furthermore, Global Performance Indicator (GPI) is used to rank the models based upon their prediction performance which ranges between −3.5626 and 4.1998. The study revealed that DYB models are best suited for hot climatic regions of Pakistan as compared to cold climatic zones. Also, the locations within the China Pakistan Economic Corridor (CPEC) highway network have maximum accuracy. This study can help energy system designers to predict DGSR values at any location in Pakistan.

KEYWORDS:

• Daily global solar radiation
• independent models
• day of year
• climatic zones
• statistical analysis

Acknowledgments

These datasets were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program and Energy Sector Management Assistance Program (ESMAP) of The World Bank Group.

Disclosure statement

The author(s) declare that they have no competing interests.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available at https://power.larc.nasa.gov/data-access-viewer/ and https://energydata.info/

Authors contributions

Muhammad Uzair Yousuf and Syed Muhammad Rashid Hussain contributed equally in conceptualization, data curation, formal analysis, investigation, resources, software, validation, and writing.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Additional information

Funding

The authors declare that they have no source of funding.

Notes on contributors

Muhammad Uzair Yousuf

Muhammad Uzair Yousuf received the B.E. and M.E. degrees in mechanical engineering from the NED University of Engineering and Technology, Karachi, Pakistan, in 2013 and 2016. He is currently pursuing a Ph.D. degree in mechanical and electrical engineering at Massey University. His current research interests include wind energy forecasting and solar energy modelling.

Syed Muhammad Rashid Hussain

Syed Muhammad Rashid Hussain received his B.E in Electrical Engineering and M.E.M in Energy Management degrees from the Department of Electrical Engineering at NED University of Engineering and Technology, Karachi, Pakistan in 2013 and 2017 respectively. He is currently working as a lecturer in the Department of Electrical Engineering at NED University of Engineering and Technology, Karachi, Pakistan. His research interests include signal processing, speech processing, artificial intelligence, data science, energy management, and forecasting.