Global horizontal irradiance prediction model for multi-site fusion under different aerosol types

Gao, Xiu-Yan; Huang, Chun-Lin; Zhang, Zhen-Huan; Chen, Qi-Xiang; Zheng, Yu; Fu, Di-Song; Yuan, Yuan

doi:10.1016/j.renene.2024.120565

Renewable Energy

2024

DOI: 10.1016/j.renene.2024.120565

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Global horizontal irradiance prediction model for multi-site fusion under different aerosol types

Xiu-Yan Gao,

Chun-Lin Huang,

Zhen-Huan Zhang

et al.

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Cited by 4 publications

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Multi-modal feature fusion model based on TimesNet and T2T-ViT for ultra-short-term solar irradiance forecasting

Li,

Ma,

Wang

et al. 2025

Renewable Energy

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Multi-modal feature fusion model based on TimesNet and T2T-ViT for ultra-short-term solar irradiance forecasting

Li,

Ma,

Wang

et al. 2025

Renewable Energy

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Comparison and optimization of electrochemical and thermal performances of SOFC with different configurations of a metal foam flow field

Jian,

He,

Mao

et al. 2024

Journal of Renewable and Sustainable Energy

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The effects of different configurations of a metal foam flow field on current density and temperature gradient of a single channel solid oxide fuel cell (SOFC) are investigated, and the overall performance of the optimum configuration is optimized. First, model A (conventional channel-rib flow field) is compared with three different configurations (i.e., model B with metal foam flow field only at anode, model C with that only at cathode, and model D at both). Although model C achieves the highest current density, its temperature is also fairly high. Although model B achieves the lowest temperature gradient, its current density is also fairly low. Model D performs well in both current density and temperature gradient. Then, the effect of electrode thickness and metal foam thickness on the performance of model D is investigated. The Ohmic polarization of model D remains almost constant with different electrode thicknesses, and its concentration polarization decreases as the electrode thickness decreases, which is totally different from the channel-rib flow field. Moreover, a thinner cathode causes lower activation overpotential, whereas a thinner anode causes higher activation overpotential. In general, a thick anode, a thin cathode, and thin metal foam can maximize the current density of model D, while a thick electrode and metal foam can always reduce the temperature gradient. Finally, the Taguchi method and gray relational analysis are used to optimize the current density and temperature gradient of model D. The current density of an optimized model is 3.27% higher than that of the original model with a temperature gradient of 9.05 K/cm.

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Accurate Forecasting of Global Horizontal Irradiance in Saudi Arabia: A Comparative Study of Machine Learning Predictive Models and Feature Selection Techniques

Imam,

Abusorrah,

Seedahmed

et al. 2024

Mathematics

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The growing interest in solar energy stems from its potential to reduce greenhouse gas emissions. Global horizontal irradiance (GHI) is a crucial determinant of the productivity of solar photovoltaic (PV) systems. Consequently, accurate GHI forecasting is essential for efficient planning, integration, and optimization of solar PV energy systems. This study evaluates the performance of six machine learning (ML) regression models—artificial neural network (ANN), decision tree (DT), elastic net (EN), linear regression (LR), Random Forest (RF), and support vector regression (SVR)—in predicting GHI for a site in northern Saudi Arabia known for its high solar energy potential. Using historical data from the NASA POWER database, covering the period from 1984 to 2022, we employed advanced feature selection techniques to enhance the predictive models. The models were evaluated based on metrics such as R-squared (R2), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The DT model demonstrated the highest performance, achieving an R2 of 1.0, MSE of 0.0, RMSE of 0.0, MAPE of 0.0%, and MAE of 0.0. Conversely, the EN model showed the lowest performance with an R2 of 0.8396, MSE of 0.4389, RMSE of 0.6549, MAPE of 9.66%, and MAE of 0.5534. While forward, backward, and exhaustive search feature selection methods generally yielded limited performance improvements for most models, the SVR model experienced significant enhancement. These findings offer valuable insights for selecting optimal forecasting strategies for solar energy projects, contributing to the advancement of renewable energy integration and supporting the global transition towards sustainable energy solutions.

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Global horizontal irradiance prediction model for multi-site fusion under different aerosol types

Cited by 4 publications

References 50 publications

Multi-modal feature fusion model based on TimesNet and T2T-ViT for ultra-short-term solar irradiance forecasting

Multi-modal feature fusion model based on TimesNet and T2T-ViT for ultra-short-term solar irradiance forecasting

Comparison and optimization of electrochemical and thermal performances of SOFC with different configurations of a metal foam flow field

Accurate Forecasting of Global Horizontal Irradiance in Saudi Arabia: A Comparative Study of Machine Learning Predictive Models and Feature Selection Techniques

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