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Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks

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Cited by:

  1. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
  2. Attia, Shady & Evrard, Arnaud & Gratia, Elisabeth, 2012. "Development of benchmark models for the Egyptian residential buildings sector," Applied Energy, Elsevier, vol. 94(C), pages 270-284.
  3. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
  4. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
  5. Yu, Biying & Zhang, Junyi & Fujiwara, Akimasa, 2011. "Representing in-home and out-of-home energy consumption behavior in Beijing," Energy Policy, Elsevier, vol. 39(7), pages 4168-4177, July.
  6. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
  7. 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).
  8. Konstantinos Papageorgiou & Elpiniki I. Papageorgiou & Katarzyna Poczeta & Dionysis Bochtis & George Stamoulis, 2020. "Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 13(9), pages 1-32, May.
  9. Al-Ghandoor, A. & Jaber, J.O. & Al-Hinti, I. & Mansour, I.M., 2009. "Residential past and future energy consumption: Potential savings and environmental impact," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1262-1274, August.
  10. Shigeru Matsumoto, 2015. "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Proceedings of International Academic Conferences 3105452, International Institute of Social and Economic Sciences.
  11. Carroll Paula & Murphy Tadhg & Hanley Michael & Dempsey Daniel & Dunne John, 2018. "Household Classification Using Smart Meter Data," Journal of Official Statistics, Sciendo, vol. 34(1), pages 1-25, March.
  12. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
  13. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
  14. Wang, Qiang & Lin, Jian & Zhou, Kan & Fan, Jie & Kwan, Mei-Po, 2020. "Does urbanization lead to less residential energy consumption? A comparative study of 136 countries," Energy, Elsevier, vol. 202(C).
  15. Olanrewaju, O.A & Jimoh, A.A, 2014. "Review of energy models to the development of an efficient industrial energy model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 661-671.
  16. Soo-Jin Lee & You-Jeong Kim & Hye-Sun Jin & Sung-Im Kim & Soo-Yeon Ha & Seung-Yeong Song, 2019. "Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis," Energies, MDPI, vol. 12(12), pages 1-18, June.
  17. Marcin Zygmunt & Dariusz Gawin, 2021. "Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock," Energies, MDPI, vol. 14(24), pages 1-15, December.
  18. Rocco, Matteo V. & Colombo, Emanuela, 2016. "Internalization of human labor in embodied energy analysis: Definition and application of a novel approach based on Environmentally extended Input-Output analysis," Applied Energy, Elsevier, vol. 182(C), pages 590-601.
  19. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
  20. Siyu Zhang & Liusan Wu & Ming Cheng & Dongqing Zhang, 2022. "Prediction of Whole Social Electricity Consumption in Jiangsu Province Based on Metabolic FGM (1, 1) Model," Mathematics, MDPI, vol. 10(11), pages 1-14, May.
  21. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
  22. 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.
  23. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
  24. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  25. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
  26. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
  27. Lee, Timothy & Yao, Runming, 2013. "Incorporating technology buying behaviour into UK-based long term domestic stock energy models to provide improved policy analysis," Energy Policy, Elsevier, vol. 52(C), pages 363-372.
  28. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
  29. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
  30. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.
  31. Muratori, Matteo & Roberts, Matthew C. & Sioshansi, Ramteen & Marano, Vincenzo & Rizzoni, Giorgio, 2013. "A highly resolved modeling technique to simulate residential power demand," Applied Energy, Elsevier, vol. 107(C), pages 465-473.
  32. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
  33. Peng, Jieyang & Kimmig, Andreas & Niu, Zhibin & Wang, Jiahai & Liu, Xiufeng & Ovtcharova, Jivka, 2021. "A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework," Applied Energy, Elsevier, vol. 299(C).
  34. Zhu, Mengshu & Huang, Ying & Wang, Si-Nuo & Zheng, Xinye & Wei, Chu, 2023. "Characteristics and patterns of residential energy consumption for space cooling in China: Evidence from appliance-level data," Energy, Elsevier, vol. 265(C).
  35. Ye, Xianming & Xia, Xiaohua & Zhang, Jiangfeng, 2013. "Optimal sampling plan for clean development mechanism energy efficiency lighting projects," Applied Energy, Elsevier, vol. 112(C), pages 1006-1015.
  36. Theofano Fotiou & Alessia de Vita & Pantelis Capros, 2019. "Economic-Engineering Modelling of the Buildings Sector to Study the Transition towards Deep Decarbonisation in the EU," Energies, MDPI, vol. 12(14), pages 1-28, July.
  37. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
  38. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2004. "Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks," Applied Energy, Elsevier, vol. 79(2), pages 159-178, October.
  39. Mahmoud, Mohamed A. & Alajmi, Ali F., 2010. "Quantitative assessment of energy conservation due to public awareness campaigns using neural networks," Applied Energy, Elsevier, vol. 87(1), pages 220-228, January.
  40. 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.
  41. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2013. "Evaluation of time series techniques to characterise domestic electricity demand," Energy, Elsevier, vol. 50(C), pages 120-130.
  42. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
  43. Ermis, K. & Midilli, A. & Dincer, I. & Rosen, M.A., 2007. "Artificial neural network analysis of world green energy use," Energy Policy, Elsevier, vol. 35(3), pages 1731-1743, March.
  44. 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.
  45. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
  46. Huebner, Gesche & Shipworth, David & Hamilton, Ian & Chalabi, Zaid & Oreszczyn, Tadj, 2016. "Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes," Applied Energy, Elsevier, vol. 177(C), pages 692-702.
  47. Filogamo, Luana & Peri, Giorgia & Rizzo, Gianfranco & Giaccone, Antonino, 2014. "On the classification of large residential buildings stocks by sample typologies for energy planning purposes," Applied Energy, Elsevier, vol. 135(C), pages 825-835.
  48. Thiaw, L. & Sow, G. & Fall, S.S. & Kasse, M. & Sylla, E. & Thioye, S., 2010. "A neural network based approach for wind resource and wind generators production assessment," Applied Energy, Elsevier, vol. 87(5), pages 1744-1748, May.
  49. Kurt, Hüseyin & Kayfeci, Muhammet, 2009. "Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks," Applied Energy, Elsevier, vol. 86(10), pages 2244-2248, October.
  50. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
  51. Macedo, M.N.Q. & Galo, J.J.M. & de Almeida, L.A.L. & de C. Lima, A.C., 2015. "Demand side management using artificial neural networks in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 128-133.
  52. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  53. 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.
  54. Kilpatrick, R.A.R. & Banfill, P.F.G. & Jenkins, D.P., 2011. "Methodology for characterising domestic electrical demand by usage categories," Applied Energy, Elsevier, vol. 88(3), pages 612-621, March.
  55. Farzan, Farbod & Jafari, Mohsen A. & Gong, Jie & Farzan, Farnaz & Stryker, Andrew, 2015. "A multi-scale adaptive model of residential energy demand," Applied Energy, Elsevier, vol. 150(C), pages 258-273.
  56. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
  57. Zhang, Wenwen & Robinson, Caleb & Guhathakurta, Subhrajit & Garikapati, Venu M. & Dilkina, Bistra & Brown, Marilyn A. & Pendyala, Ram M., 2018. "Estimating residential energy consumption in metropolitan areas: A microsimulation approach," Energy, Elsevier, vol. 155(C), pages 162-173.
  58. Salata, Ferdinando & Golasi, Iacopo & di Salvatore, Maicol & de Lieto Vollaro, Andrea, 2016. "Energy and reliability optimization of a system that combines daylighting and artificial sources. A case study carried out in academic buildings," Applied Energy, Elsevier, vol. 169(C), pages 250-266.
  59. Shi, Lei & Zhang, Shuai & Arshad, Adeel & Hu, Yanwei & He, Yurong & Yan, Yuying, 2021. "Thermo-physical properties prediction of carbon-based magnetic nanofluids based on an artificial neural network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
  60. Shen, Pengyuan & Yang, Biao, 2020. "Projecting Texas energy use for residential sector under future climate and urbanization scenarios: A bottom-up method based on twenty-year regional energy use data," Energy, Elsevier, vol. 193(C).
  61. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
  62. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
  63. Shaker Zabada & Isam Shahrour, 2017. "Analysis of Heating Expenses in a Large Social Housing Stock Using Artificial Neural Networks," Energies, MDPI, vol. 10(12), pages 1-8, December.
  64. Morrissey, J. & Moore, T. & Horne, R.E., 2011. "Affordable passive solar design in a temperate climate: An experiment in residential building orientation," Renewable Energy, Elsevier, vol. 36(2), pages 568-577.
  65. Hong, Taehoon & Koo, Choongwan & Kim, Daeho & Lee, Minhyun & Kim, Jimin, 2015. "An estimation methodology for the dynamic operational rating of a new residential building using the advanced case-based reasoning and stochastic approaches," Applied Energy, Elsevier, vol. 150(C), pages 308-322.
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