Abstract
AbstractRecently, 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.
Funder
City of Scientific Research and Technological Applications
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Economics and Econometrics,Geography, Planning and Development
Reference95 articles.
1. Abdo, T., & El-Shimy, M. (2013). Estimating the global solar radiation for solar energy projects–Egypt case study. International Journal of Sustainable Energy, 32(6), 682–712. https://doi.org/10.1080/14786451.2013.822872
2. Agarwal, V., Malhotra, S., Dagar, V., & Pavithra, M. R. (2023). Coping with public-private partnership issues: A path forward to sustainable agriculture. Socio-Economic Planning Sciences, 89, 101703. https://doi.org/10.1016/j.seps.2023.101703
3. Ajayi, O. O., Ohijeagbon, O. D., Nwadialo, C. E., & Olasope, O. (2014). New model to estimate daily global solar radiation over Nigeria. Sustainable Energy Technologies and Assessments, 5, 28–36. https://doi.org/10.1016/j.seta.2013.11.001
4. Ali, M. A., Hassan, G. E., & Youssef, M. E. (2016). Assessment the performance of artificial neural networks in estimating global solar radiation. In International conference on new trends for sustainable energy-ICNTSE (pp. 148–150).
5. Ali, Z., Qingmei, B., Hafiz, T., & Kamran, W. (2022). A multi-perspective assessment approach of renewable energy production: Policy perspective analysis. Environment, Development and Sustainability, 24(2), 2164–2192. https://doi.org/10.1007/s10668-021-01524-8
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