Solar Engineering Of Thermal Processes

Solar radiation is a primary driver for many physical, chemical, and biological processes on the earth’s surface. Solar energy engineers, architects, agriculturists, hydrologists, etc. often require a reasonably accurate knowledge of the availability of the solar resource for their relevant applications at their local. In solar applications, one of the most important parameters needed is the long-term average daily global irradiation. For regions where no actual measured values are available, a common practice is to estimate average daily global solar radiation using appropriate empirical correlations based on the measured relevant data at those locations. These correlations estimate the values of global solar radiation for a region of interest from more readily available meteorological, climatological, and geographical parameters. The main objective of this study is to chronologically collect and review the extensive global solar radiation models available in the literature and to classify them into four categories, i.e., sunshine-based, cloud-based, temperature-based, and other meteorological parameter-based models, based on the employed meteorological parameters as model input.

Furthermore, in order to evaluate the accuracy and applicability of the models reported in this paper for computing the monthly average daily global solar radiation on a horizontal surface, the geographical and meteorological data of Yazd city, Iran was used. The developed models were then evaluated and compared on the basis of statistical error indices and the most accurate model was chosen in each category. Results revealed that all the proposed correlations have a good estimation of the monthly average daily global solar radiation on a horizontal surface in Yazd city, however, the El-Metwally sunshine-based model predicts the monthly averaged global solar radiation with a higher accuracy.

Empirical models for estimating global solar radiation: A review and case study

Application of support vector machine models for forecasting solar and wind energy resources: A review

Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Accurate estimation of reference evapotranspiration is essential for agricultural management and water resources engineering applications. In the present study, the ability and precision of three artificial intelligence (AI) models (i.e., Support Vector Machines (SVMs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Categorical Boosting (CatBoost)) were assessed for estimating daily reference evapotranspiration (ET0) using limited weather data from five locations in a warm sub-humid climate in Mexico. The Penman–Monteith FAO-56 equation was used as a reference target for ET0 values. Three different input combinations were investigated, namely: temperature-based (minimum and maximum air temperature), rainfall-based (minimum air temperature, maximum air temperature and rainfall), and relative humidity-based (minimum air temperature, maximum air temperature and relative humidity). Extraterrestrial radiation values were used in all combinations. The temperature-based AI models were compared with the conventional Hargreaves–Samani (HS) model commonly used to estimate ET0 when only temperature records are available. The goodness of fit for all models was assessed in terms of the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), root mean square error (RMSE) and mean absolute error (MAE). The results showed that among the AI models evaluated, the SVM models outperformed ANFIS and CatBoost for modeling ET0. Further, the influence of relative humidity and rainfall on the performance of the models was investigated. The analysis indicated that relative humidity significantly improved the accuracy of the models. Finally, the results showed a better response of the temperature-based AI models over the HS method. AI models can be an adequate alternative to conventional models for ET0 modeling.

https://riviste.fupress.net/index.php/IJAm/article/download/1373/1220

Evaluation of artificial intelligence models for daily prediction of reference evapotranspiration using temperature, rainfall and relative humidity in a warm sub-humid environment

Misuses of the terms Day of the Year and Julian Day in agricultural and environmental sciences

Closure to “Evaluation of Temperature-Based Methods for the Estimation of Reference Evapotranspiration in the Yucatán Peninsula, Mexico” by Victor H. Quej, Javier Almorox, Javier A. Arnaldo, and Rubén Moratiel

Javier Almorox

Papers

Evaluation of artificial intelligence models for daily prediction of reference evapotranspiration using temperature, rainfall and relative humidity in a warm sub-humid environment

Misuses of the terms Day of the Year and Julian Day in agricultural and environmental sciences

Closure to “Evaluation of Temperature-Based Methods for the Estimation of Reference Evapotranspiration in the Yucatán Peninsula, Mexico” by Victor H. Quej, Javier Almorox, Javier A. Arnaldo, and Rubén Moratiel