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Building thermal load prediction through shallow machine learning and deep learning

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  1. 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).
  2. Jieyun Zheng & Linyao Zhang & Jinpeng Chen & Guilian Wu & Shiyuan Ni & Zhijian Hu & Changhong Weng & Zhi Chen, 2021. "Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM," Energies, MDPI, vol. 14(8), pages 1-14, April.
  3. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
  4. Jia, Lizhi & Liu, Junjie & Chong, Adrian & Dai, Xilei, 2022. "Deep learning and physics-based modeling for the optimization of ice-based thermal energy systems in cooling plants," Applied Energy, Elsevier, vol. 322(C).
  5. 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).
  6. Neubauer, Alexander & Brandt, Stefan & Kriegel, Martin, 2024. "Relationship between feature importance and building characteristics for heating load predictions," Applied Energy, Elsevier, vol. 359(C).
  7. 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).
  8. Sun, Fangyuan & Kong, Xiangyu & Wu, Jianzhong & Gao, Bixuan & Chen, Ke & Lu, Ning, 2022. "DSM pricing method based on A3C and LSTM under cloud-edge environment," Applied Energy, Elsevier, vol. 315(C).
  9. Peng Ye & Bin Yu & Wenhong Chen & Kan Liu & Longzhen Ye, 2022. "Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 965-995, September.
  10. Zhang, Lidong & Li, Jiao & Xu, Xiandong & Liu, Fengrui & Guo, Yuanjun & Yang, Zhile & Hu, Tianyu, 2023. "High spatial granularity residential heating load forecast based on Dendrite net model," Energy, Elsevier, vol. 269(C).
  11. Kyoungcheol Oh & Eui-Jong Kim & Chang-Young Park, 2022. "A Physical Model-Based Data-Driven Approach to Overcome Data Scarcity and Predict Building Energy Consumption," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
  12. 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.
  13. Mateo Jesper & Felix Pag & Klaus Vajen & Ulrike Jordan, 2022. "Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modeling," Sustainability, MDPI, vol. 14(7), pages 1-32, March.
  14. Zhou, Yuan & Wang, Jiangjiang & Liu, Yi & Yan, Rujing & Ma, Yanpeng, 2021. "Incorporating deep learning of load predictions to enhance the optimal active energy management of combined cooling, heating and power system," Energy, Elsevier, vol. 233(C).
  15. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  16. Li, Sihui & Peng, Jinqing & Zou, Bin & Li, Bojia & Lu, Chujie & Cao, Jingyu & Luo, Yimo & Ma, Tao, 2021. "Zero energy potential of photovoltaic direct-driven air conditioners with considering the load flexibility of air conditioners," Applied Energy, Elsevier, vol. 304(C).
  17. William Mounter & Chris Ogwumike & Huda Dawood & Nashwan Dawood, 2021. "Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study," Energies, MDPI, vol. 14(18), pages 1-42, September.
  18. Bruck, Axel & Díaz Ruano, Santiago & Auer, Hans, 2022. "One piece of the puzzle towards 100 Positive Energy Districts (PEDs) across Europe by 2025: An open-source approach to unveil favourable locations of PV-based PEDs from a techno-economic perspective," Energy, Elsevier, vol. 254(PA).
  19. Liu, Yiren & Zhao, Xiangyu & Qin, S. Joe, 2024. "Dynamically engineered multi-modal feature learning for predictions of office building cooling loads," Applied Energy, Elsevier, vol. 355(C).
  20. Seyfi, Mohammad & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Shayanfar, Heidarali, 2022. "Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles," Applied Energy, Elsevier, vol. 321(C).
  21. Wei, Ziqing & Zhang, Tingwei & Yue, Bao & Ding, Yunxiao & Xiao, Ran & Wang, Ruzhu & Zhai, Xiaoqiang, 2021. "Prediction of residential district heating load based on machine learning: A case study," Energy, Elsevier, vol. 231(C).
  22. Huang, Guizao & Wu, Guangning & Yang, Zefeng & Chen, Xing & Wei, Wenfu, 2023. "Development of surrogate models for evaluating energy transfer quality of high-speed railway pantograph-catenary system using physics-based model and machine learning," Applied Energy, Elsevier, vol. 333(C).
  23. Christos D. Kaskouras & Konstantinos F. Krommydas & Ioannis Baltas & George P. Papaioannou & Georgios I. Papayiannis & Athanasios N. Yannacopoulos, 2024. "Assessing the Flexibility of Power Systems through Neural Networks: A Study of the Hellenic Transmission System," Sustainability, MDPI, vol. 16(14), pages 1-21, July.
  24. Manuel Jaramillo & Wilson Pavón & Lisbeth Jaramillo, 2024. "Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review," Data, MDPI, vol. 9(1), pages 1-23, January.
  25. 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).
  26. Lin, Xiaojie & Mao, Yihui & Chen, Jiaying & Zhong, Wei, 2023. "Dynamic modeling and uncertainty quantification of district heating systems considering renewable energy access," Applied Energy, Elsevier, vol. 349(C).
  27. Xiaoyu Lin & Hang Yu & Meng Wang & Chaoen Li & Zi Wang & Yin Tang, 2021. "Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method," Energies, MDPI, vol. 14(16), pages 1-21, August.
  28. Connor Scott & Mominul Ahsan & Alhussein Albarbar, 2021. "Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings," Sustainability, MDPI, vol. 13(7), pages 1-22, April.
  29. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.
  30. Li, Tian & Bie, Haipei & Lu, Yi & Sawyer, Azadeh Omidfar & Loftness, Vivian, 2024. "MEBA: AI-powered precise building monthly energy benchmarking approach," Applied Energy, Elsevier, vol. 359(C).
  31. Luo, Yongqiang & Yan, Tian & Zhang, Nan, 2020. "Study on dynamic thermal characteristics of thermoelectric radiant cooling panel system through a hybrid method," Energy, Elsevier, vol. 208(C).
  32. Eşlik, Ardan Hüseyin & Akarslan, Emre & Hocaoğlu, Fatih Onur, 2022. "Short-term solar radiation forecasting with a novel image processing-based deep learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 1490-1505.
  33. 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).
  34. Chun-Wei Chen & Chun-Chang Li & Chen-Yu Lin, 2020. "Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System," Energies, MDPI, vol. 13(17), pages 1-20, August.
  35. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
  36. Hossein Moayedi & Amir Mosavi, 2021. "Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings," Energies, MDPI, vol. 14(5), pages 1-25, March.
  37. Yuwen You & Zhonghua Wang & Zhihao Liu & Chunmei Guo & Bin Yang, 2024. "Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network," Energies, MDPI, vol. 17(16), pages 1-19, August.
  38. Valeria Todeschi & Roberto Boghetti & Jérôme H. Kämpf & Guglielmina Mutani, 2021. "Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
  39. Cui, Mianshan, 2022. "District heating load prediction algorithm based on bidirectional long short-term memory network model," Energy, Elsevier, vol. 254(PA).
  40. Ng, Rong Wang & Begam, Kasim Mumtaj & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2021. "An improved self-organizing incremental neural network model for short-term time-series load prediction," Applied Energy, Elsevier, vol. 292(C).
  41. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
  42. Li, Guannan & Wu, Yubei & Yoon, Sungmin & Fang, Xi, 2024. "Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning," Energy, Elsevier, vol. 299(C).
  43. Adam Kula & Albert Smalcerz & Maciej Sajkowski & Zygmunt Kamiński, 2021. "Analysis of Office Rooms Energy Consumption Data in Respect to Meteorological and Direct Sun Exposure Conditions," Energies, MDPI, vol. 14(22), pages 1-20, November.
  44. Tomaž Čegovnik & Andrej Dobrovoljc & Janez Povh & Matic Rogar & Pavel Tomšič, 2023. "Electricity consumption prediction using artificial intelligence," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(3), pages 833-851, September.
  45. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
  46. Cao, Hui & Lin, Jiajing & Li, Nan, 2023. "Optimal control and energy efficiency evaluation of district ice storage system," Energy, Elsevier, vol. 276(C).
  47. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
  48. Xuesong Tian & Yuping Zou & Xin Wang & Minglang Tseng & Hua Li & Huijuan Zhang, 2022. "Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
  49. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
  50. Ng, Rong Wang & Begam, K.M. & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2022. "A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions," Energy, Elsevier, vol. 248(C).
  51. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).
  52. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Self-learning dynamic graph neural network with self-attention based on historical data and future data for multi-task multivariate residential air conditioning forecasting," Applied Energy, Elsevier, vol. 364(C).
  53. Hossein Moayedi & Amir Mosavi, 2021. "Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings," Energies, MDPI, vol. 14(6), pages 1-19, March.
  54. Luca Brunelli & Emiliano Borri & Anna Laura Pisello & Andrea Nicolini & Carles Mateu & Luisa F. Cabeza, 2024. "Thermal Energy Storage in Energy Communities: A Perspective Overview through a Bibliometric Analysis," Sustainability, MDPI, vol. 16(14), pages 1-27, July.
  55. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
  56. Zhang, Chaobo & Li, Junyang & Zhao, Yang & Li, Tingting & Chen, Qi & Zhang, Xuejun & Qiu, Weikang, 2021. "Problem of data imbalance in building energy load prediction: Concept, influence, and solution," Applied Energy, Elsevier, vol. 297(C).
  57. Aristeidis Mystakidis & Paraskevas Koukaras & Nikolaos Tsalikidis & Dimosthenis Ioannidis & Christos Tjortjis, 2024. "Energy Forecasting: A Comprehensive Review of Techniques and Technologies," Energies, MDPI, vol. 17(7), pages 1-33, March.
  58. Lange, Jelto & Kaltschmitt, Martin, 2022. "Probabilistic day-ahead forecast of available thermal storage capacities in residential households," Applied Energy, Elsevier, vol. 306(PA).
  59. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
  60. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
  61. Liang, Xinbin & Chen, Siliang & Zhu, Xu & Jin, Xinqiao & Du, Zhimin, 2023. "Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions," Applied Energy, Elsevier, vol. 344(C).
  62. Chen, Minghao & Xie, Zhiyuan & Sun, Yi & Zheng, Shunlin, 2023. "The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting," Applied Energy, Elsevier, vol. 350(C).
  63. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).
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