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Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector

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  1. Hedegaard, Rasmus Elbæk & Kristensen, Martin Heine & Pedersen, Theis Heidmann & Brun, Adam & Petersen, Steffen, 2019. "Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response," Applied Energy, Elsevier, vol. 242(C), pages 181-204.
  2. Scott Kelly, 2011. "Do homes that are more energy efficient consume less energy?: A structural equation model for England's residential sector," Working Papers EPRG 1117, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
  3. Papineau, Maya & Yassin, Kareman & Newsham, Guy & Brice, Sarah, 2021. "Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
  4. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
  5. Kelly, Scott, 2011. "Do homes that are more energy efficient consume less energy?: A structural equation model of the English residential sector," Energy, Elsevier, vol. 36(9), pages 5610-5620.
  6. Antanasijević, Davor & Pocajt, Viktor & Ristić, Mirjana & Perić-Grujić, Aleksandra, 2015. "Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks," Energy, Elsevier, vol. 84(C), pages 816-824.
  7. 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.
  8. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
  9. Li, Y.P. & Huang, G.H. & Chen, X., 2011. "Planning regional energy system in association with greenhouse gas mitigation under uncertainty," Applied Energy, Elsevier, vol. 88(3), pages 599-611, March.
  10. Turki Alajmi & Patrick Phelan, 2020. "Modeling and Forecasting End-Use Energy Consumption for Residential Buildings in Kuwait Using a Bottom-Up Approach," Energies, MDPI, vol. 13(8), pages 1-19, April.
  11. Sen, Doruk & Günay, M. Erdem & Tunç, K.M. Murat, 2019. "Forecasting annual natural gas consumption using socio-economic indicators for making future policies," Energy, Elsevier, vol. 173(C), pages 1106-1118.
  12. 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.
  13. Zeng, Yaohui & Zhang, Zijun & Kusiak, Andrew, 2015. "Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms," Energy, Elsevier, vol. 86(C), pages 393-402.
  14. 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.
  15. 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.
  16. Hannah Goozee, 2017. "Energy, poverty and development: a primer for the Sustainable Development Goals," Working Papers 156, International Policy Centre for Inclusive Growth.
  17. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
  18. Heeren, Niko & Jakob, Martin & Martius, Gregor & Gross, Nadja & Wallbaum, Holger, 2013. "A component based bottom-up building stock model for comprehensive environmental impact assessment and target control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 45-56.
  19. 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.
  20. 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.
  21. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & A. Wood, David, 2021. "Global natural gas demand to 2025: A learning scenario development model," Energy, Elsevier, vol. 224(C).
  22. Tomasz Szul & Sylwester Tabor & Krzysztof Pancerz, 2021. "Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating," Energies, MDPI, vol. 14(10), pages 1-13, May.
  23. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
  24. 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.
  25. Zhu, Y. & Huang, G.H. & Li, Y.P. & He, L. & Zhang, X.X., 2011. "An interval full-infinite mixed-integer programming method for planning municipal energy systems - A case study of Beijing," Applied Energy, Elsevier, vol. 88(8), pages 2846-2862, August.
  26. 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.
  27. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
  28. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
  29. Chen, Kang & Zhu, Xu & Anduv, Burkay & Jin, Xinqiao & Du, Zhimin, 2022. "Digital twins model and its updating method for heating, ventilation and air conditioning system using broad learning system algorithm," Energy, Elsevier, vol. 251(C).
  30. 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.
  31. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
  32. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
  33. Theodoridou, Ifigeneia & Papadopoulos, Agis M. & Hegger, Manfred, 2012. "A feasibility evaluation tool for sustainable cities – A case study for Greece," Energy Policy, Elsevier, vol. 44(C), pages 207-216.
  34. Newsham, Guy R. & Donnelly, Cara L., 2013. "A model of residential energy end-use in Canada: Using conditional demand analysis to suggest policy options for community energy planners," Energy Policy, Elsevier, vol. 59(C), pages 133-142.
  35. 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.
  36. Barbeito, Inés & Zaragoza, Sonia & Tarrío-Saavedra, Javier & Naya, Salvador, 2017. "Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data," Applied Energy, Elsevier, vol. 190(C), pages 1-17.
  37. Ian H. Rowlands & Tobi Reid & Paul Parker, 2015. "Research with disaggregated electricity end‐use data in households: review and recommendations," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 4(5), pages 383-396, September.
  38. Ilaria Ballarini & Vincenzo Corrado, 2017. "A New Methodology for Assessing the Energy Consumption of Building Stocks," Energies, MDPI, vol. 10(8), pages 1-22, July.
  39. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
  40. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  41. Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
  42. Luis Gonzaga Baca Ruiz & Manuel Pegalajar Cuéllar & Miguel Delgado Calvo-Flores & María Del Carmen Pegalajar Jiménez, 2016. "An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings," Energies, MDPI, vol. 9(9), pages 1-21, August.
  43. 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.
  44. Verdejo, Humberto & Awerkin, Almendra & Becker, Cristhian & Olguin, Gabriel, 2017. "Statistic linear parametric techniques for residential electric energy demand forecasting. A review and an implementation to Chile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 512-521.
  45. Mir Hossein Mousavi, 2015. "An Estimation of Natural Gas Demand in Household Sector of Iran; the Structural Time Series Approach," Proceedings of International Academic Conferences 2804383, International Institute of Social and Economic Sciences.
  46. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
  47. 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.
  48. Kovačič, Miha & Šarler, Božidar, 2014. "Genetic programming prediction of the natural gas consumption in a steel plant," Energy, Elsevier, vol. 66(C), pages 273-284.
  49. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
  50. Pedone, Livio & Molaioni, Filippo & Vallati, Andrea & Pampanin, Stefano, 2023. "Energy refurbishment planning of Italian school buildings using data-driven predictive models," Applied Energy, Elsevier, vol. 350(C).
  51. Jin Woo Moon & Sung Kwon Jung & Yong Oh Lee & Sangsun Choi, 2015. "Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms," Energies, MDPI, vol. 8(8), pages 1-18, August.
  52. 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.
  53. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
  54. 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.
  55. Allinson, David & Irvine, Katherine N. & Edmondson, Jill L. & Tiwary, Abhishek & Hill, Graeme & Morris, Jonathan & Bell, Margaret & Davies, Zoe G. & Firth, Steven K. & Fisher, Jill & Gaston, Kevin J. , 2016. "Measurement and analysis of household carbon: The case of a UK city," Applied Energy, Elsevier, vol. 164(C), pages 871-881.
  56. Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
  57. Lee, Soo-Jin & Song, Seung-Yeong, 2022. "Time-series analysis of the effects of building and household features on residential end-use energy," Applied Energy, Elsevier, vol. 312(C).
  58. 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.
  59. 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.
  60. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
  61. 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.
  62. Amar Bennadji & Mohammed Seddiki & Jamal Alabid & Richard Laing & David Gray, 2022. "Predicting Energy Savings of the UK Housing Stock under a Step-by-Step Energy Retrofit Scenario towards Net-Zero," Energies, MDPI, vol. 15(9), pages 1-18, April.
  63. Dineen, D. & Rogan, F. & Ó Gallachóir, B.P., 2015. "Improved modelling of thermal energy savings potential in the existing residential stock using a newly available data source," Energy, Elsevier, vol. 90(P1), pages 759-767.
  64. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
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