Author
Javier Almorox
Bio: Javier Almorox is an academic researcher from Technical University of Madrid. The author has contributed to research in topics: Evapotranspiration & Mean squared error. The author has an hindex of 16, co-authored 33 publications receiving 1493 citations.
Papers
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TL;DR: In this paper, several equations were employed to estimate global solar radiation from sunshine hours for 16 meteorological stations in Spain, using only the relative duration of sunshine, including the original Angstrom-Prescott linear regression and modified functions (quadratic, third degree, logarithmic and exponential functions).
374 citations
TL;DR: In this paper, the accuracy and performance of three soft computing techniques (i.e., adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM)) were assessed for predicting daily horizontal global solar radiation from measured meteorological variables in the Yucatan Peninsula, Mexico.
205 citations
TL;DR: In this paper, the authors developed a new model for estimating global solar radiation data using temperature measured data for seven stations located in Madrid, Spain, which can be used in the design and estimation of the performance of solar applications.
159 citations
TL;DR: In this article, the root mean square error (RMSE), the mean bias error (MBE), and the t -statistic were used to estimate the specific monthly global solar radiation.
138 citations
TL;DR: In this article, the applicability of different TET approaches was evaluated in relation to the Koppen climate classification of the stations, considering a monthly timescale and FAO56 Penman Monteith benchmarks (PM56).
124 citations
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TL;DR: In this article, a chronologically collected and reviewed the extensive global solar radiation models available in the literature and to classify them into four categories, i.e., sunshine-based, cloudbased, temperature-based and other meteorological parameter-based models, based on the employed meteorological parameters as model input.
Abstract: 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.
388 citations
TL;DR: In this article, a hybrid support vector machine (SVM) model was proposed to forecast both solar and wind energy resources for most of the locations in the United States, where the authors highlighted main problems, opportunities and future work in this research area.
350 citations
TL;DR: Wang et al. as discussed by the authors proposed two machine learning algorithms, i.e., Support Vector Machine (SVM) and a novel simple tree-based ensemble method named Extreme Gradient Boosting (XGBoost), for accurate prediction of daily H using limited meteorological data.
345 citations
TL;DR: There is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models.
Abstract: 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.
300 citations