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Artificial neural networks in energy applications in buildings

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  • Soteris A. Kalogirou

Abstract

Artificial neural networks (ANNs) are nowadays accepted as an alternative technology offering a way to tackle complex and ill-defined problems. They are not programmed in the traditional way but they are trained using past history data representing the behaviour of a system. They have been used in a number of diverse applications. Results presented in this paper are testimony to the potential of artificial neural networks as a design tool in many areas of building services engineering. Copyright , Manchester University Press.

Suggested Citation

  • Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
    • Handle: RePEc:oup:ijlctc:v:1:y:2006:i:3:p:201-216
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    1. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    2. Linda Barelli & Elisa Belloni & Gianni Bidini & Cinzia Buratti & Emilia Maria Pinchi, 2021. "Development of a Decisional Procedure Based on Fuzzy Logic for the Energy Retrofitting of Buildings," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    3. Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    4. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    5. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    6. Barone, G. & Buonomano, A. & Forzano, C. & Giuzio, G.F. & Palombo, A. & Russo, G., 2023. "A new thermal comfort model based on physiological parameters for the smart design and control of energy-efficient HVAC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    7. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    8. Hamid R. Khosravani & María Del Mar Castilla & Manuel Berenguel & Antonio E. Ruano & Pedro M. Ferreira, 2016. "A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building," Energies, MDPI, vol. 9(1), pages 1-24, January.
    9. Zini, Marco & Carcasci, Carlo, 2023. "Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy," Energy, Elsevier, vol. 262(PB).
    10. Selimefendigil, Fatih & Öztop, Hakan F., 2021. "Thermoelectric generation in bifurcating channels and efficient modeling by using hybrid CFD and artificial neural networks," Renewable Energy, Elsevier, vol. 172(C), pages 582-598.
    11. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    12. 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.
    13. Yanxia Li & Chao Wang & Sijie Zhu & Junyan Yang & Shen Wei & Xinkai Zhang & Xing Shi, 2020. "A Comparison of Various Bottom-Up Urban Energy Simulation Methods Using a Case Study in Hangzhou, China," Energies, MDPI, vol. 13(18), pages 1-23, September.
    14. Kwok Wai Mui & Ling Tim Wong & Manoj Kumar Satheesan & Anjana Balachandran, 2021. "A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong," Energies, MDPI, vol. 14(16), pages 1-18, August.
    15. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
    16. Sergio Ortega Alba & Mario Manana, 2017. "Characterization and Analysis of Energy Demand Patterns in Airports," Energies, MDPI, vol. 10(1), pages 1-35, January.
    17. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shrahily, Raid, 2016. "Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 761-776.
    18. Tosin T. Oye & Naren Gupta & Keng Goh & Toyosi K. Oye, 2022. "Development of Sustainable Indoor Air Quality for Air-Conditioning System Using Smart Control Techniques," Environmental Management and Sustainable Development, Macrothink Institute, vol. 11(1), pages 1-37, December.
    19. Israr Ullah & Rashid Ahmad & DoHyeun Kim, 2018. "A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model," Energies, MDPI, vol. 11(2), pages 1-20, February.
    20. Zekić-Sušac Marijana & Scitovski Rudolf & Has Adela, 2018. "Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 4(2), pages 57-66, November.
    21. Bordbari, Mohammad Javad & Seifi, Ali Reza & Rastegar, Mohammad, 2018. "Probabilistic energy consumption analysis in buildings using point estimate method," Energy, Elsevier, vol. 142(C), pages 716-722.
    22. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    23. Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
    24. Wong, Ross Y.M. & Tso, C.Y. & Jeong, S.Y. & Fu, S.C. & Chao, Christopher Y.H., 2023. "Critical sky temperatures for passive radiative cooling," Renewable Energy, Elsevier, vol. 211(C), pages 214-226.
    25. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.

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