Application of artificial neural networks in horizontal luminous efficacy modeling
Author
Abstract
Suggested Citation
DOI: 10.1016/j.renene.2022.08.016
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Li, Danny H.W. & Chen, Wenqiang & Li, Shuyang & Lou, Siwei, 2019. "Estimation of hourly global solar radiation using Multivariate Adaptive Regression Spline (MARS) – A case study of Hong Kong," Energy, Elsevier, vol. 186(C).
- Wang, Huakeer & Lu, Wei & Wu, Zhigen & Zhang, Guanhua, 2020. "Parametric analysis of applying PCM wallboards for energy saving in high-rise lightweight buildings in Shanghai," Renewable Energy, Elsevier, vol. 145(C), pages 52-64.
- Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
- Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
- Li, Danny H.W. & Lou, Siwei, 2018. "Review of solar irradiance and daylight illuminance modeling and sky classification," Renewable Energy, Elsevier, vol. 126(C), pages 445-453.
- Fakra, A.H. & Boyer, H. & Miranville, F. & Bigot, D., 2011. "A simple evaluation of global and diffuse luminous efficacy for all sky conditions in tropical and humid climate," Renewable Energy, Elsevier, vol. 36(1), pages 298-306.
- Azad, Abdus Salam & Rakshit, Dibakar & Patil, K.N., 2018. "Model development and evaluation of global and diffuse luminous efficacy for humid sub-tropical region," Renewable Energy, Elsevier, vol. 119(C), pages 375-387.
- Tsikaloudaki, Katerina, 2005. "A study on luminous efficacy of global radiation under clear sky conditions in Athens, Greece," Renewable Energy, Elsevier, vol. 30(4), pages 551-563.
- Janjai, S. & Prathumsit, J. & Buntoung, S. & Wattan, R. & Pattarapanitchai, S. & Masiri, I., 2014. "Modeling the luminous efficacy of direct and diffuse solar radiation using information on cloud, aerosol and water vapor in the tropics," Renewable Energy, Elsevier, vol. 66(C), pages 111-117.
- Dieste-Velasco, M.I. & Díez-Mediavilla, M. & Granados-López, D. & González-Peña, D. & Alonso-Tristán, C., 2019. "Performance of global luminous efficacy models and proposal of a new model for daylighting in Burgos, Spain," Renewable Energy, Elsevier, vol. 133(C), pages 1000-1010.
- Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
- Siwei Lou & Yu Huang & Dawei Xia & Isaac Y F Lun & Danny H W Li, 2021. "A study of the skylight coverage ratio for air-conditioned atriumsin the hot and humid regions," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 16(3), pages 946-955.
- Dieste-Velasco, M.I. & Díez-Mediavilla, M. & Alonso-Tristán, C. & González-Peña, D. & Rodríguez-Amigo, M.C. & García-Calderón, T., 2020. "A new diffuse luminous efficacy model for daylight availability in Burgos, Spain," Renewable Energy, Elsevier, vol. 146(C), pages 2812-2826.
- Chaiwiwatworakul, Pipat & Chirarattananon, Surapong, 2013. "Luminous efficacies of global and diffuse horizontal irradiances in a tropical region," Renewable Energy, Elsevier, vol. 53(C), pages 148-158.
- Zhang, Weilong & Lu, Lin & Peng, Jinqing, 2017. "Evaluation of potential benefits of solar photovoltaic shadings in Hong Kong," Energy, Elsevier, vol. 137(C), pages 1152-1158.
- Li, Danny H.W. & Lam, Tony N.T. & Cheung, K.L. & Tang, H.L., 2008. "An analysis of luminous efficacies under the CIE standard skies," Renewable Energy, Elsevier, vol. 33(11), pages 2357-2365.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Dieste-Velasco, M.I. & Díez-Mediavilla, M. & Alonso-Tristán, C. & González-Peña, D. & Rodríguez-Amigo, M.C. & García-Calderón, T., 2020. "A new diffuse luminous efficacy model for daylight availability in Burgos, Spain," Renewable Energy, Elsevier, vol. 146(C), pages 2812-2826.
- Dieste-Velasco, M.I. & Díez-Mediavilla, M. & Granados-López, D. & González-Peña, D. & Alonso-Tristán, C., 2019. "Performance of global luminous efficacy models and proposal of a new model for daylighting in Burgos, Spain," Renewable Energy, Elsevier, vol. 133(C), pages 1000-1010.
- Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Ma, Xin & Bai, Hua, 2019. "Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 168-186.
- Azad, Abdus Salam & Rakshit, Dibakar & Patil, K.N., 2018. "Model development and evaluation of global and diffuse luminous efficacy for humid sub-tropical region," Renewable Energy, Elsevier, vol. 119(C), pages 375-387.
- Alrubaih, M.S. & Zain, M.F.M. & Alghoul, M.A. & Ibrahim, N.L.N. & Shameri, M.A. & Elayeb, Omkalthum, 2013. "Research and development on aspects of daylighting fundamentals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 494-505.
- Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
- Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).
- Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).
- Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
- Dong, Juan & Xing, Liwen & Cui, Ningbo & Guo, Li & Liang, Chuan & Zhao, Lu & Wang, Zhihui & Gong, Daozhi, 2024. "Estimating reference crop evapotranspiration using optimized empirical methods with a novel improved Grey Wolf Algorithm in four climatic regions of China," Agricultural Water Management, Elsevier, vol. 291(C).
- Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2024. "An interpretable framework for modeling global solar radiation using tree-based ensemble machine learning and Shapley additive explanations methods," Applied Energy, Elsevier, vol. 364(C).
- Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Dong Eun Jung & Chanuk Lee & Kwang Ho Lee & Minjae Shin & Sung Lok Do, 2021. "Evaluation of Building Energy Performance with Optimal Control of Movable Shading Device Integrated with PV System," Energies, MDPI, vol. 14(7), pages 1-21, March.
- Dehwah, Ammar H.A. & Krarti, Moncef, 2021. "Energy performance of integrated adaptive envelope systems for residential buildings," Energy, Elsevier, vol. 233(C).
- Li, Danny H.W. & Lam, Tony N.T. & Chan, Wilco W.H. & Mak, Ada H.L., 2009. "Energy and cost analysis of semi-transparent photovoltaic in office buildings," Applied Energy, Elsevier, vol. 86(5), pages 722-729, May.
- Szafranek, Karol, 2019.
"Bagged neural networks for forecasting Polish (low) inflation,"
International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
- Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
- Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
- Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
- Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
More about this item
Keywords
Daylighting; Luminous efficacy; Machine learning; Empirical model; Sensitivity analysis;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:197:y:2022:i:c:p:864-878. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.