An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery
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DOI: 10.1016/j.energy.2015.08.043
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Cited by:
- Oh, Myeongchan & Kim, Chang Ki & Kim, Boyoung & Yun, Changyeol & Kim, Jin-Young & Kang, Yongheack & Kim, Hyun-Goo, 2022. "Analysis of minute-scale variability for enhanced separation of direct and diffuse solar irradiance components using machine learning algorithms," Energy, Elsevier, vol. 241(C).
- Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
- Sholahudin, S. & Han, Hwataik, 2016. "Simplified dynamic neural network model to predict heating load of a building using Taguchi method," Energy, Elsevier, vol. 115(P3), pages 1672-1678.
- Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Applied Energy, Elsevier, vol. 203(C), pages 897-916.
- Rodríguez, Eduardo & Droguett, Enrique López & Cardemil, José M. & Starke, Allan R. & Cornejo-Ponce, Lorena, 2024. "Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models," Renewable Energy, Elsevier, vol. 231(C).
- Xwégnon Ghislain Agoua & Robin Girard & Georges Kariniotakis, 2021. "Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy," Energies, MDPI, vol. 14(5), pages 1-15, March.
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Keywords
Direct normal solar radiation; Evolutionary artificial neural network; Ensemble model; Meteosat images; Satellite-derived irradiances;All these keywords.
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