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A review on the prediction of building energy consumption

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  1. Santos, Luis Guilherme Resende & Afshari, Afshin & Norford, Leslie K. & Mao, Jiachen, 2018. "Evaluating approaches for district-wide energy model calibration considering the Urban Heat Island effect," Applied Energy, Elsevier, vol. 215(C), pages 31-40.
  2. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
  3. Jonathan Roth & Jayashree Chadalawada & Rishee K. Jain & Clayton Miller, 2021. "Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification," Energies, MDPI, vol. 14(5), pages 1-22, March.
  4. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
  5. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
  6. Zhang, Yan & Teoh, Bak Koon & Wu, Maozhi & Chen, Jiayu & Zhang, Limao, 2023. "Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence," Energy, Elsevier, vol. 262(PA).
  7. Milen Balbis-Morejón & Juan J. Cabello-Eras & Javier M. Rey-Hernández & Francisco J. Rey-Martínez, 2021. "Energy Evaluation and Energy Savings Analysis with the 2 Selection of AC Systems in an Educational Building," Sustainability, MDPI, vol. 13(14), pages 1-10, July.
  8. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
  9. Chul-Ho Kim & Seung-Eon Lee & Kwang-Ho Lee & Kang-Soo Kim, 2019. "Detailed Comparison of the Operational Characteristics of Energy-Conserving HVAC Systems during the Cooling Season," Energies, MDPI, vol. 12(21), pages 1-29, October.
  10. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
  11. Zhang, Tiantian & Tan, Yufei & Yang, Hongxing & Zhang, Xuedan, 2016. "The application of air layers in building envelopes: A review," Applied Energy, Elsevier, vol. 165(C), pages 707-734.
  12. Jaroslav Košičan & Miguel Ángel Pardo Picazo & Silvia Vilčeková & Danica Košičanová, 2021. "Life Cycle Assessment and Economic Energy Efficiency of a Solar Thermal Installation in a Family House," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
  13. Li Huang & Rongyue Zheng & Udo Piontek, 2019. "Installation and Operation of a Solar Cooling and Heating System Incorporated with Air-Source Heat Pumps," Energies, MDPI, vol. 12(6), pages 1-17, March.
  14. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.
  15. 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).
  16. Zhang, Yuhang & Zhang, Yi & Yi Zhang, & Zhang, Chengxu, 2022. "Effect of physical, environmental, and social factors on prediction of building energy consumption for public buildings based on real-world big data," Energy, Elsevier, vol. 261(PB).
  17. Carolina Rodriguez & María Coronado & Marta D’Alessandro & Juan Medina, 2019. "The Importance of Standardised Data-Collection Methods in the Improvement of Thermal Comfort Assessment Models for Developing Countries in the Tropics," Sustainability, MDPI, vol. 11(15), pages 1-22, August.
  18. 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.
  19. Li, Sihui & Gong, Guangcai & Peng, Jinqing, 2019. "Dynamic coupling method between air-source heat pumps and buildings in China’s hot-summer/cold-winter zone," Applied Energy, Elsevier, vol. 254(C).
  20. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
  21. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  22. Abhinandana Boodi & Karim Beddiar & Malek Benamour & Yassine Amirat & Mohamed Benbouzid, 2018. "Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations," Energies, MDPI, vol. 11(10), pages 1-26, September.
  23. Li, Xiaoma & Zhou, Yuyu & Yu, Sha & Jia, Gensuo & Li, Huidong & Li, Wenliang, 2019. "Urban heat island impacts on building energy consumption: A review of approaches and findings," Energy, Elsevier, vol. 174(C), pages 407-419.
  24. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
  25. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
  26. Murray, D.M. & Liao, J. & Stankovic, L. & Stankovic, V., 2016. "Understanding usage patterns of electric kettle and energy saving potential," Applied Energy, Elsevier, vol. 171(C), pages 231-242.
  27. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
  28. Wang, Wei & Chen, Jiayu & Huang, Gongsheng & Lu, Yujie, 2017. "Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution," Applied Energy, Elsevier, vol. 207(C), pages 305-323.
  29. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
  30. Fu, Hongxiang & Baltazar, Juan-Carlos & Claridge, David E., 2021. "Review of developments in whole-building statistical energy consumption models for commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
  31. Nikolaos Barmparesos & Dimitra Papadaki & Michalis Karalis & Kyriaki Fameliari & Margarita Niki Assimakopoulos, 2019. "In Situ Measurements of Energy Consumption and Indoor Environmental Quality of a Pre-Retrofitted Student Dormitory in Athens," Energies, MDPI, vol. 12(11), pages 1-19, June.
  32. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
  33. Amedeo Buonanno & Martina Caliano & Antonino Pontecorvo & Gianluca Sforza & Maria Valenti & Giorgio Graditi, 2022. "Global vs. Local Models for Short-Term Electricity Demand Prediction in a Residential/Lodging Scenario," Energies, MDPI, vol. 15(6), pages 1-18, March.
  34. Tschopp, Daniel & Tian, Zhiyong & Berberich, Magdalena & Fan, Jianhua & Perers, Bengt & Furbo, Simon, 2020. "Large-scale solar thermal systems in leading countries: A review and comparative study of Denmark, China, Germany and Austria," Applied Energy, Elsevier, vol. 270(C).
  35. Zhang, Yang & Campana, Pietro Elia & Yang, Ying & Stridh, Bengt & Lundblad, Anders & Yan, Jinyue, 2018. "Energy flexibility from the consumer: Integrating local electricity and heat supplies in a building," Applied Energy, Elsevier, vol. 223(C), pages 430-442.
  36. Xiaodong Yang & Jiayu Zhang & Xianbo Zhao, 2018. "Factors Affecting Green Residential Building Development: Social Network Analysis," Sustainability, MDPI, vol. 10(5), pages 1-21, May.
  37. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
  38. Ali Movahedi & Sybil Derrible, 2021. "Interrelationships between electricity, gas, and water consumption in large‐scale buildings," Journal of Industrial Ecology, Yale University, vol. 25(4), pages 932-947, August.
  39. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
  40. Walter, Travis & Sohn, Michael D., 2016. "A regression-based approach to estimating retrofit savings using the Building Performance Database," Applied Energy, Elsevier, vol. 179(C), pages 996-1005.
  41. Gde Dharma Nugraha & Ardiansyah Musa & Jaiyoung Cho & Kishik Park & Deokjai Choi, 2018. "Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings," Energies, MDPI, vol. 11(4), pages 1-20, March.
  42. Vogler–Finck, P.J.C. & Bacher, P. & Madsen, H., 2017. "Online short-term forecast of greenhouse heat load using a weather forecast service," Applied Energy, Elsevier, vol. 205(C), pages 1298-1310.
  43. Charani Shandiz, Saeid & Rismanchi, Behzad & Foliente, Greg, 2021. "Energy master planning for net-zero emission communities: State of the art and research challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  44. José Sánchez Ramos & MCarmen Guerrero Delgado & Servando Álvarez Domínguez & José Luis Molina Félix & Francisco José Sánchez de la Flor & José Antonio Tenorio Ríos, 2019. "Systematic Simplified Simulation Methodology for Deep Energy Retrofitting Towards Nze Targets Using Life Cycle Energy Assessment," Energies, MDPI, vol. 12(16), pages 1-27, August.
  45. Raatikainen, Mika & Skön, Jukka-Pekka & Leiviskä, Kauko & Kolehmainen, Mikko, 2016. "Intelligent analysis of energy consumption in school buildings," Applied Energy, Elsevier, vol. 165(C), pages 416-429.
  46. Lei, Lei & Wu, Bing & Fang, Xin & Chen, Li & Wu, Hao & Liu, Wei, 2023. "A dynamic anomaly detection method of building energy consumption based on data mining technology," Energy, Elsevier, vol. 263(PA).
  47. Liangwen Yan & Fengfeng Qian & Wei Li, 2018. "Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm," Energies, MDPI, vol. 12(1), pages 1-13, December.
  48. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
  49. 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.
  50. Maltais, Louis-Gabriel & Gosselin, Louis, 2022. "Forecasting of short-term lighting and plug load electricity consumption in single residential units: Development and assessment of data-driven models for different horizons," Applied Energy, Elsevier, vol. 307(C).
  51. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  52. 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.
  53. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
  54. Pickering, B. & Choudhary, R., 2019. "District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles," Applied Energy, Elsevier, vol. 236(C), pages 1138-1157.
  55. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  56. Eleftheria Touloupaki & Theodoros Theodosiou, 2017. "Performance Simulation Integrated in Parametric 3D Modeling as a Method for Early Stage Design Optimization—A Review," Energies, MDPI, vol. 10(5), pages 1-18, May.
  57. Spiliotis, Evangelos & Petropoulos, Fotios & Kourentzes, Nikolaos & Assimakopoulos, Vassilios, 2018. "Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption," MPRA Paper 91762, University Library of Munich, Germany.
  58. 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).
  59. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
  60. Rick Cox & Shalika Walker & Joep van der Velden & Phuong Nguyen & Wim Zeiler, 2020. "Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids," Energies, MDPI, vol. 13(9), pages 1-27, May.
  61. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
  62. Tronchin, Lamberto & Manfren, Massimiliano & Nastasi, Benedetto, 2018. "Energy efficiency, demand side management and energy storage technologies – A critical analysis of possible paths of integration in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 341-353.
  63. Le Cam, M. & Zmeureanu, R. & Daoud, A., 2017. "Cascade-based short-term forecasting method of the electric demand of HVAC system," Energy, Elsevier, vol. 119(C), pages 1098-1107.
  64. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Liu, Hongwu & Wang, Cheng, 2020. "An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control," Energy, Elsevier, vol. 199(C).
  65. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
  66. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  67. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
  68. Mahmud, Khizir & Amin, Uzma & Hossain, M.J. & Ravishankar, Jayashri, 2018. "Computational tools for design, analysis, and management of residential energy systems," Applied Energy, Elsevier, vol. 221(C), pages 535-556.
  69. Guelpa, Elisa & Marincioni, Ludovica & Capone, Martina & Deputato, Stefania & Verda, Vittorio, 2019. "Thermal load prediction in district heating systems," Energy, Elsevier, vol. 176(C), pages 693-703.
  70. Lee, Sang Hoon & Hong, Tianzhen & Piette, Mary Ann & Taylor-Lange, Sarah C., 2015. "Energy retrofit analysis toolkits for commercial buildings: A review," Energy, Elsevier, vol. 89(C), pages 1087-1100.
  71. Yang, Tianren & Zhang, Xiaoling, 2016. "Benchmarking the building energy consumption and solar energy trade-offs of residential neighborhoods on Chongming Eco-Island, China," Applied Energy, Elsevier, vol. 180(C), pages 792-799.
  72. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  73. Severinsen, A. & Myrland, Ø., 2022. "Statistical learning to estimate energy savings from retrofitting in the Norwegian food retail market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  74. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
  75. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
  76. Guillaume Guerard & Hugo Pousseur & Ihab Taleb, 2021. "Isolated Areas Consumption Short-Term Forecasting Method," Energies, MDPI, vol. 14(23), pages 1-23, November.
  77. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
  78. 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.
  79. de Christo, Tiago Malavazi & Perron, Sylvain & Fardin, Jussara Farias & Simonetti, Domingos Sávio Lyrio & de Alvarez, Cristina Engel, 2019. "Demand-side energy management by cooperative combination of plans: A multi-objective method applicable to isolated communities," Applied Energy, Elsevier, vol. 240(C), pages 453-472.
  80. 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).
  81. Saurabh Singh & Pradip Kumar Sharma & Jong Hyuk Park, 2017. "SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home," Sustainability, MDPI, vol. 9(4), pages 1-19, March.
  82. Huang, Runya & Huang, Guohe & Cheng, Guanhui & Dong, Cong, 2017. "Regional heuristic interval recourse power system analysis for electricity and environmental systems planning in Eastern China," Resources, Conservation & Recycling, Elsevier, vol. 122(C), pages 185-201.
  83. Xu, Yuanjin & Li, Fei & Asgari, Armin, 2022. "Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms," Energy, Elsevier, vol. 240(C).
  84. 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.
  85. Jihoon Moon & Sungwoo Park & Seungmin Rho & Eenjun Hwang, 2019. "A comparative analysis of artificial neural network architectures for building energy consumption forecasting," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
  86. Tam, Vivian W.Y. & Le, Khoa N. & Zeng, S.X. & Wang, Xiangyu & Illankoon, I.M. Chethana S., 2017. "Regenerative practice of using photovoltaic solar systems for residential dwellings: An empirical study in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1-10.
  87. Orosz, Matthew & Altes-Buch, Queralt & Mueller, Amy & Lemort, Vincent, 2018. "Experimental validation of an electrical and thermal energy demand model for rapid assessment of rural health centers in sub-Saharan Africa," Applied Energy, Elsevier, vol. 218(C), pages 382-390.
  88. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
  89. Saeid Charani Shandiz & Alice Denarie & Gabriele Cassetti & Marco Calderoni & Antoine Frein & Mario Motta, 2019. "A Simplified Methodology for Existing Tertiary Buildings’ Cooling Energy Need Estimation at District Level: A Feasibility Study of a District Cooling System in Marrakech," Energies, MDPI, vol. 12(5), pages 1-20, March.
  90. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
  91. Fazeli, Reza & Davidsdottir, Brynhildur & Hallgrimsson, Jonas Hlynur, 2016. "Residential energy demand for space heating in the Nordic countries: Accounting for interfuel substitution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1210-1226.
  92. Aniela Kaminska, 2019. "Impact of Heating Control Strategy and Occupant Behavior on the Energy Consumption in a Building with Natural Ventilation in Poland," Energies, MDPI, vol. 12(22), pages 1-18, November.
  93. Abu Bakar, Nur Najihah & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah & Bandi, Masilah, 2015. "Energy efficiency index as an indicator for measuring building energy performance: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 1-11.
  94. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
  95. 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.
  96. Zhao, Deyin & Zhong, Ming & Zhang, Xu & Su, Xing, 2016. "Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining," Energy, Elsevier, vol. 102(C), pages 660-668.
  97. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  98. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.
  99. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
  100. Chan-Joong Kim & Taehoon Hong & Jimin Kim & Daeho Kim & Dong-yeon Seo, 2015. "A Process for the Implementation of New Renewable Energy Systems in a Building by Considering Environmental and Economic Effect," Sustainability, MDPI, vol. 7(9), pages 1-21, September.
  101. Shilei Lu & Minchao Fan & Yiqun Zhao, 2018. "A System to Pre-Evaluate the Suitability of Energy-Saving Technology for Green Buildings," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
  102. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
  103. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
  104. Salah Bouktif & Ali Ouni & Sanja Lazarova-Molnar, 2022. "Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview," Energies, MDPI, vol. 15(5), pages 1-30, February.
  105. Ole Øiene Smedegård & Thomas Jonsson & Bjørn Aas & Jørn Stene & Laurent Georges & Salvatore Carlucci, 2021. "The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway," Energies, MDPI, vol. 14(16), pages 1-24, August.
  106. 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).
  107. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
  108. Roth, Jonathan & Rajagopal, Ram, 2018. "Benchmarking building energy efficiency using quantile regression," Energy, Elsevier, vol. 152(C), pages 866-876.
  109. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
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