Estimation of solar radiation in different climates of Iran using hybrid machine learning methods

علوم کاربردی و محاسباتی در مکانیک

Article 3, Volume 35, Issue 2 - Serial Number 32, July 2023, Pages 37-54   

Document Type: Original Article

DOI: 10.22067/jacsm.2022.79378.1144

Authors

Nabi Jahantigh^{* 1}; jamshid Piri²

¹Department of Mechanical Engineering, Faculty of Technology and Engineering, Zabol University, Zabol

²Department of Water Engineering, Faculty Water and Soil, Zabol University, Zabol, Iran.

Abstract

Solar radiation, the basic parameter required for solar energy programs, is not measured in all parts of the world due to technical and financial limitations, and it is necessary to determine with the accurate estimation methods. In this research, a model was developed to estimate the daily horizontal scattered solar radiation with the hybrid methods of Support Vector Regression, Gray Wolf Optimization and Harmony Search Algorithm. To determine the validity of the model, daily measured solar radiation data of different cities in the sunny part of Iran (Bandar Abbas, Kerman, Sanandaj, Semnan and Zahedan) were used. The input parameters of the models are the mean temperature, relative humidity, sunshine hour, evaporation and wind velocity. The comparison of the solar radiation of the hybrid model with the measured values shows the desired results based on statistical analysis, and the hybrid model is an efficient and accurate method compared to other models, especially experimental models. The average absolute bias error obtained, root mean square error and correlation coefficient for Zahedan station are respectively equal to 14.77 MJ⁄m^2, 26.85 MJ⁄m^2, and 0.68 for the experimental equation and the obtained values of the regression vector machine - Harmony respectively It was similar to 13.85MJ⁄m^2, 9.58 MJ⁄m^2 and 0.71. The research results showed that the regression-harmony vector machine model is an efficient method that is much more accurate than other models.

Keywords

Solar Radiation; climate change; Support Vector Regression; Wolf Optimization Algorithm; Harmony Search Algorithm; Experimental Models

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