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A review of data-driven building energy consumption prediction studies

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Cited by:

  1. Rohács, Dániel, 2023. "Analysis and optimization of potential energy sources for residential building application," Energy, Elsevier, vol. 275(C).
  2. Huang, Yaohui & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng & Liu, Hanjing & Fu, Yonggang, 2023. "Explainable district heat load forecasting with active deep learning," Applied Energy, Elsevier, vol. 350(C).
  3. 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).
  4. 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).
  5. Gökhan Demirdöğen & Zeynep Işık & Yusuf Arayici, 2020. "Lean Management Framework for Healthcare Facilities Integrating BIM, BEPS and Big Data Analytics," Sustainability, MDPI, vol. 12(17), pages 1-33, August.
  6. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
  7. 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).
  8. Tang, Lingfeng & Xie, Haipeng & Wang, Xiaoyang & Bie, Zhaohong, 2023. "Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach," Applied Energy, Elsevier, vol. 337(C).
  9. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
  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. Lu, Chujie & Li, Sihui & Reddy Penaka, Santhan & Olofsson, Thomas, 2023. "Automated machine learning-based framework of heating and cooling load prediction for quick residential building design," Energy, Elsevier, vol. 274(C).
  12. 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).
  13. Sarabia Escriva, Emilio José & Hart, Matthew & Acha, Salvador & Soto Francés, Víctor & Shah, Nilay & Markides, Christos N., 2022. "Techno-economic evaluation of integrated energy systems for heat recovery applications in food retail buildings," Applied Energy, Elsevier, vol. 305(C).
  14. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
  15. Chun-Wei Chen, 2023. "A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?," Sustainability, MDPI, vol. 15(5), pages 1-21, February.
  16. Ganapathy Ramesh & Jaganathan Logeshwaran & Thangavel Kiruthiga & Jaime Lloret, 2023. "Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
  17. Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumai Chelvan, 2024. "Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review," Energies, MDPI, vol. 17(3), pages 1-20, January.
  18. Tuukka Salmi & Jussi Kiljander & Daniel Pakkala, 2020. "Stacked Boosters Network Architecture for Short-Term Load Forecasting in Buildings," Energies, MDPI, vol. 13(9), pages 1-15, May.
  19. Koschwitz, D. & Frisch, J. & van Treeck, C., 2018. "Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale," Energy, Elsevier, vol. 165(PA), pages 134-142.
  20. Kang, Hyuna & An, Jongbaek & Kim, Hakpyeong & Ji, Changyoon & Hong, Taehoon & Lee, Seunghye, 2021. "Changes in energy consumption according to building use type under COVID-19 pandemic in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
  21. Ihab Taleb & Guillaume Guerard & Frédéric Fauberteau & Nga Nguyen, 2022. "A Flexible Deep Learning Method for Energy Forecasting," Energies, MDPI, vol. 15(11), pages 1-16, May.
  22. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
  23. 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).
  24. Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
  25. Zhaocheng Li & Yu Song, 2022. "Energy Consumption Linkages of the Chinese Construction Sector," Energies, MDPI, vol. 15(5), pages 1-13, February.
  26. 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.
  27. Cao, Jingyu & Zheng, Ling & Peng, Jinqing & Wang, Wenjie & Leung, Michael K.H. & Zheng, Zhanying & Hu, Mingke & Wang, Qiliang & Cai, Jingyong & Pei, Gang & Ji, Jie, 2023. "Advances in coupled use of renewable energy sources for performance enhancement of vapour compression heat pump: A systematic review of applications to buildings," Applied Energy, Elsevier, vol. 332(C).
  28. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
  29. Muhammad Aslam Jarwar & Sajjad Ali & Ilyoung Chong, 2019. "Microservices Model to Enhance the Availability of Data for Buildings Energy Efficiency Management Services," Energies, MDPI, vol. 12(3), pages 1-27, January.
  30. Akhtari, Mohammad Reza & Shayegh, Iman & Karimi, Nader, 2020. "Techno-economic assessment and optimization of a hybrid renewable earth - air heat exchanger coupled with electric boiler, hydrogen, wind and PV configurations," Renewable Energy, Elsevier, vol. 148(C), pages 839-851.
  31. 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.
  32. 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).
  33. Michel Noussan & Benedetto Nastasi, 2018. "Data Analysis of Heating Systems for Buildings—A Tool for Energy Planning, Policies and Systems Simulation," Energies, MDPI, vol. 11(1), pages 1-15, January.
  34. Aiman Mohammed & Muhammad Atiq Ur Rehman Tariq & Anne Wai Man Ng & Zeeshan Zaheer & Safwan Sadeq & Mahmood Mohammed & Hooman Mehdizadeh-Rad, 2022. "Reducing the Cooling Loads of Buildings Using Shading Devices: A Case Study in Darwin," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
  35. Sol Kim & Sungwon Jung & Seung-Man Baek, 2019. "A Model for Predicting Energy Usage Pattern Types with Energy Consumption Information According to the Behaviors of Single-Person Households in South Korea," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
  36. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
  37. Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
  38. Abdulrahman Alanezi & Kevin P. Hallinan & Rodwan Elhashmi, 2021. "Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings," Energies, MDPI, vol. 14(1), pages 1-16, January.
  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. Jacob Hale & Suzanna Long, 2020. "A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition," Energies, MDPI, vol. 14(1), pages 1-14, December.
  41. Joaquín Garrido-Zafra & Antonio Moreno-Munoz & Aurora Gil-de-Castro & Emilio J. Palacios-Garcia & Carlos D. Moreno-Moreno & Tomás Morales-Leal, 2019. "A Novel Direct Load Control Testbed for Smart Appliances," Energies, MDPI, vol. 12(17), pages 1-16, August.
  42. Amna Shibeika & Maatouk Khoukhi & Omar Al Khatib & Nouf Alzahmi & Shamma Tahnoon & Maryam Al Dhahri & Nouf Alshamsi, 2021. "Integrated Design Process for High-Performance Buildings; a Case Study from Dubai," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
  43. Michel Noussan & Roberta Roberto & Benedetto Nastasi, 2018. "Performance Indicators of Electricity Generation at Country Level—The Case of Italy," Energies, MDPI, vol. 11(3), pages 1-14, March.
  44. Sesil Koutra & Christos S. Ioakimidis, 2022. "Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges," Land, MDPI, vol. 12(1), pages 1-19, December.
  45. Fan, Cheng & Xiao, Fu & Song, Mengjie & Wang, Jiayuan, 2019. "A graph mining-based methodology for discovering and visualizing high-level knowledge for building energy management," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  46. Sang-Woo Ha & Seung-Hoon Park & Jae-Yong Eom & Min-Suk Oh & Ga-Young Cho & Eui-Jong Kim, 2020. "Parameter Calibration for a TRNSYS BIPV Model Using In Situ Test Data," Energies, MDPI, vol. 13(18), pages 1-15, September.
  47. Serik Tokbolat & Farnush Nazipov & Jong R. Kim & Ferhat Karaca, 2019. "Evaluation of the Environmental Performance of Residential Building Envelope Components," Energies, MDPI, vol. 13(1), pages 1-10, December.
  48. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
  49. Hazar, Hanbey & Tekdogan, Remziye & Sevinc, Huseyin, 2021. "Determination of the effects of oxygen-enriched air with the help of zeolites on the exhaust emission and performance of a diesel engine," Energy, Elsevier, vol. 236(C).
  50. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
  51. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Hoare, Cathal & Purcell, Karl & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings," Applied Energy, Elsevier, vol. 267(C).
  52. Tien, Paige Wenbin & Wei, Shuangyu & Calautit, John Kaiser & Darkwa, Jo & Wood, Christopher, 2022. "Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand," Applied Energy, Elsevier, vol. 308(C).
  53. 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).
  54. Amasyali, Kadir & El-Gohary, Nora, 2021. "Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
  55. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
  56. Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
  57. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Mohammed Elsayed Lotfy & Theophilus Amara & Keifa Vamba Konneh & Tomonobu Senjyu, 2019. "Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning," Future Internet, MDPI, vol. 11(10), pages 1-16, October.
  58. Gleydson de Oliveira Cavalcanti & Handson Claudio Dias Pimenta, 2023. "Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review," Energies, MDPI, vol. 16(15), pages 1-29, August.
  59. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
  60. dos Santos Ferreira, Greicili & Martins dos Santos, Deilson & Luciano Avila, Sérgio & Viana Luiz Albani, Vinicius & Cardoso Orsi, Gustavo & Cesar Cordeiro Vieira, Pedro & Nilson Rodrigues, Rafael, 2023. "Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios," Applied Energy, Elsevier, vol. 339(C).
  61. Andrzej Pacana & Karolina Czerwińska & Grzegorz Ostasz, 2023. "Analysis of the Level of Efficiency of Control Methods in the Context of Energy Intensity," Energies, MDPI, vol. 16(8), pages 1-26, April.
  62. Abokersh, Mohamed Hany & Spiekman, Marleen & Vijlbrief, Olav & van Goch, T.A.J. & Vallès, Manel & Boer, Dieter, 2021. "A real-time diagnostic tool for evaluating the thermal performance of nearly zero energy buildings," Applied Energy, Elsevier, vol. 281(C).
  63. Fath U Min Ullah & Noman Khan & Tanveer Hussain & Mi Young Lee & Sung Wook Baik, 2021. "Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework," Mathematics, MDPI, vol. 9(6), pages 1-22, March.
  64. 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).
  65. Shamim Akhtar & Muhamad Zahim Bin Sujod & Syed Sajjad Hussain Rizvi, 2022. "An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms," Energies, MDPI, vol. 15(15), pages 1-19, August.
  66. Malte Stienecker & Anne Hagemeier, 2023. "Developing Feedforward Neural Networks as Benchmark for Load Forecasting: Methodology Presentation and Application to Hospital Heat Load Forecasting," Energies, MDPI, vol. 16(4), pages 1-22, February.
  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. 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).
  69. 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).
  70. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
  71. Guillaume Guerard & Hugo Pousseur & Ihab Taleb, 2021. "Isolated Areas Consumption Short-Term Forecasting Method," Energies, MDPI, vol. 14(23), pages 1-23, November.
  72. 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).
  73. Maher Selim & Ryan Zhou & Wenying Feng & Peter Quinsey, 2021. "Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design," Energies, MDPI, vol. 14(1), pages 1-15, January.
  74. 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).
  75. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  76. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
  77. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2019. "Predictive management for energy supply networks using photovoltaics, heat pumps, and battery by two-stage stochastic programming and rule-based control," Energy, Elsevier, vol. 179(C), pages 1302-1319.
  78. 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).
  79. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
  80. Marcin Relich & Arkadiusz Gola & Małgorzata Jasiulewicz-Kaczmarek, 2022. "Identifying Improvement Opportunities in Product Design for Reducing Energy Consumption," Energies, MDPI, vol. 15(24), pages 1-19, December.
  81. Moudgil, Vipul & Hewage, Kasun & Hussain, Syed Asad & Sadiq, Rehan, 2023. "Integration of IoT in building energy infrastructure: A critical review on challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
  82. Wate, P. & Iglesias, M. & Coors, V. & Robinson, D., 2020. "Framework for emulation and uncertainty quantification of a stochastic building performance simulator," Applied Energy, Elsevier, vol. 258(C).
  83. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
  84. Achini Shanika Weerasinghe & Eziaku Onyeizu Rasheed & James Olabode Bamidele Rotimi, 2023. "Occupants’ Decision-Making of Their Energy Behaviours in Office Environments: A Case of New Zealand," Sustainability, MDPI, vol. 15(3), pages 1-27, January.
  85. Zhang, Guiqing & Tian, Chenlu & Li, Chengdong & Zhang, Jun Jason & Zuo, Wangda, 2020. "Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature," Energy, Elsevier, vol. 201(C).
  86. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2023. "A Bayesian deep-learning framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 348(C).
  87. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
  88. 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.
  89. Meng Wang & Junqi Yu & Meng Zhou & Wei Quan & Renyin Cheng, 2023. "Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM," Sustainability, MDPI, vol. 15(24), pages 1-23, December.
  90. Athila Santos & Na Liu & Muhyiddine Jradi, 2021. "AUSTRET: An Automated Step Response Testing Tool for Building Automation and Control Systems," Energies, MDPI, vol. 14(13), pages 1-20, July.
  91. Saidjon Shiralievich Tavarov & Pavel Matrenin & Murodbek Safaraliev & Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane, 2023. "Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
  92. Anand Krishnan Prakash & Susu Xu & Ram Rajagopal & Hae Young Noh, 2018. "Robust Building Energy Load Forecasting Using Physically-Based Kernel Models," Energies, MDPI, vol. 11(4), pages 1-21, April.
  93. Gebrail Bekdaş & Yaren Aydın & Ümit Isıkdağ & Aidin Nobahar Sadeghifam & Sanghun Kim & Zong Woo Geem, 2023. "Prediction of Cooling Load of Tropical Buildings with Machine Learning," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
  94. Nweye, Kingsley & Nagy, Zoltan, 2022. "MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering," Applied Energy, Elsevier, vol. 316(C).
  95. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
  96. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
  97. Attila Kostyák & Csaba Béres & Szabolcs Szekeres & Imre Csáky, 2022. "Buffer Tank Discharge Strategies in the Case of a Centrifugal Water Chiller," Energies, MDPI, vol. 16(1), pages 1-15, December.
  98. Zhang, Xiaohai & Ramírez-Mendiola, José Luis & Li, Mingtao & Guo, Liejin, 2022. "Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study," Applied Energy, Elsevier, vol. 308(C).
  99. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
  100. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
  101. Ion-Costinel Mareș & Tiberiu Catalina & Marian-Andrei Istrate & Alexandra Cucoș & Tiberius Dicu & Betty Denissa Burghele & Kinga Hening & Lelia Letitia Popescu & Razvan Stefan Popescu, 2021. "Research on Best Solution for Improving Indoor Air Quality and Reducing Energy Consumption in a High-Risk Radon Dwelling from Romania," IJERPH, MDPI, vol. 18(23), pages 1-18, November.
  102. Rosenfelder, Markus & Wussow, Moritz & Gust, Gunther & Cremades, Roger & Neumann, Dirk, 2021. "Predicting residential electricity consumption using aerial and street view images," Applied Energy, Elsevier, vol. 301(C).
  103. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
  104. Muideen Adegoke & Alaka Hafiz & Saheed Ajayi & Razak Olu-Ajayi, 2022. "Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction," Energies, MDPI, vol. 15(24), pages 1-21, December.
  105. Guo, Jing & Lin, Penghui & Zhang, Limao & Pan, Yue & Xiao, Zhonghua, 2023. "Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption," Applied Energy, Elsevier, vol. 350(C).
  106. Maatouk Khoukhi & Abeer Fuad Darsaleh & Sara Ali, 2020. "Retrofitting an Existing Office Building in the UAE Towards Achieving Low-Energy Building," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
  107. Mohsen Sharifi & Amin Kouti & Evi Lambie & Yixiao Ma & Maria Fernandez Boneta & Mohammad Haris Shamsi, 2023. "A Comprehensive Framework for Data-Driven Building End-Use Assessment Utilizing Monitored Operational Parameters," Energies, MDPI, vol. 16(20), pages 1-23, October.
  108. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
  109. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
  110. Altieri, Domenico & Patel, Martin K. & Lazarus, Joël & Branca, Giovanni, 2023. "Numerical analysis of low-cost optimization measures for improving energy efficiency in residential buildings," Energy, Elsevier, vol. 273(C).
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  112. Amasyali, Kadir & El-Gohary, Nora M., 2021. "Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort," Applied Energy, Elsevier, vol. 302(C).
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