Evaluation and performance comparison of different models for global solar radiation forecasting

A case study on five cities

Bibliographic Data

ID15109656
AuthorsMohamed A Ali (0000-0002-6325-9605, Egypt-Japan University of Science and Technology, corresponding author), Ashraf Elsayed (0000-0003-2122-767X, AlAlamein International University), Islam Elkabani (0000-0001-8065-3404, AlAlamein International University), M Elsayed Youssef (0000-0003-4362-4801, City of Scientific Research and Technological Applications), Gasser E Hassan (0000-0001-5946-1512, Egypt-Japan University of Science and Technology)
Year2023
Volume27
Issue5
Pages10159-10201
Publication date2023-12-29
Open AccessYes
TypeARTICLE
VenueEnvironment Development and Sustainability (JOURNAL)
Journal identifiersISSN: 1387-585X • E-ISSN: 1573-2975
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s10668-023-04307-5
OpenAlexW4390409884
LanguageEN
References cited86

Recently, solar energy has emerged as the most promising renewable energy source to meet the world’s energy demands. However, to harness the potential of solar energy, accurate data on solar radiation are crucial. It is considered the first step in assessing solar resources for various applications and achieving energy sustainability goals. Due to the unavailability of solar radiation measurements in many parts of the world, several models have been developed to predict global solar radiation (GSR) at these locations. Thus, this study aims to evaluate the proficiency of several GSR models at five new locations and determine the most suitable one for GSR prediction. The study has further developed solar radiation models for these new locations, as well as general ones for the entire region, which does not have any GSR models despite the existence of many planned solar energy facilities in this area. Additionally, the study investigates the effect of changing the length of the validation dataset on models’ performance and accuracy, as well as assesses the introduced models’ generalization capability. To achieve these objectives, the observed data of GSR for approximately 37 years at studied locations are used to develop and validate the proposed models. The study’s findings reveal that Model 1 provides the best performance at all locations, with accuracy, coefficient of determination ( $$R^{2}$$ R 2 ), ranging from 95 to 98%, except for the coastal location, where it is from 91 to 95%. The remaining performance indicators of the best models, such as RMSE, MABE, MAPE, and $$r$$ r , are good, and their values range from 0.7863 to 1.9097 (MJ m −2 day −1 ), from 0.6430 to 1.7060 (MJ m −2 day −1 ), from 3.4319 to 10.0890 (%), and from 0.9914 to 0.9981, respectively. The length of the validation dataset has a slight effect on the models’ performance, ranging from about 1% to 2%. Therefore, Model 1 is the recommended solar radiation model, which can provide precise and rapid estimates of global solar radiation. This approach could be used in the design and performance evaluation of many solar applications. The primary benefit of this approach in the current investigation is that temperature data are continuously and effortlessly recorded for various purposes

Electrical engineering · Generalization · Machine learning · Meteorology · Optics · Physics · Predictive modelling · Renewable energy · Solar energy · Statistics · Unavailability · Computer Science · Energy and Environment Impacts · Energy Load and Power Forecasting · Engineering · Environmental Science · Mathematics · Solar Radiation and Photovoltaics · Radiation

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