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Machine learning-based thermal response time ahead energy demand prediction for building heating systems

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Cited by:

  1. 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).
  2. 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.
  3. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
  4. Sun, Chunhua & Yan, Hao & Cao, Shanshan & Xia, Guoqiang & Liu, Yanan & Wu, Xiangdong, 2024. "A control strategy considering buildings’ thermal characteristics to mitigate heat supply-demand mismatches in district heating systems," Energy, Elsevier, vol. 307(C).
  5. Wirich Freppel & Geoffrey Promis & Anh Dung Tran Le & Omar Douzane & Thierry Langlet, 2022. "Development of a Novel Experimental Facility to Assess Heating Systems’ Behaviour in Buildings," Energies, MDPI, vol. 15(13), pages 1-22, June.
  6. 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.
  7. 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).
  8. El-Baz, Wessam & Tzscheutschler, Peter & Wagner, Ulrich, 2019. "Integration of energy markets in microgrids: A double-sided auction with device-oriented bidding strategies," Applied Energy, Elsevier, vol. 241(C), pages 625-639.
  9. Ghafoori, Mahdi & Abdallah, Moatassem & Kim, Serena, 2023. "Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system," Applied Energy, Elsevier, vol. 340(C).
  10. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
  11. Jiale Tang & Kuixing Liu & Weijie You & Xinyu Zhang & Tuomi Zhang, 2023. "Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining," Energies, MDPI, vol. 16(14), pages 1-19, July.
  12. Afroz, Zakia & Urmee, Tania & Shafiullah, G.M. & Higgins, Gary, 2018. "Real-time prediction model for indoor temperature in a commercial building," Applied Energy, Elsevier, vol. 231(C), pages 29-53.
  13. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
  14. Zhao, Haitao & Ezeh, Collins I. & Ren, Weijia & Li, Wentao & Pang, Cheng Heng & Zheng, Chenghang & Gao, Xiang & Wu, Tao, 2019. "Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg0 sensing materials," Applied Energy, Elsevier, vol. 254(C).
  15. Guiqiang Wang & Haiman Wang & Zhiqiang Kang & Guohui Feng, 2020. "Data-Driven Optimization for Capacity Control of Multiple Ground Source Heat Pump System in Heating Mode," Energies, MDPI, vol. 13(14), pages 1-15, July.
  16. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
  17. Fabra, Natalia & Lacuesta, Aitor & Souza, Mateus, 2022. "The implicit cost of carbon abatement during the COVID-19 pandemic," European Economic Review, Elsevier, vol. 147(C).
  18. Hamza Mubarak & Mohammad J. Sanjari & Sascha Stegen & Abdallah Abdellatif, 2023. "Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study," Energies, MDPI, vol. 16(21), pages 1-32, October.
  19. Zihao Li & Daniel Friedrich & Gareth P. Harrison, 2020. "Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model," Energies, MDPI, vol. 13(4), pages 1-20, February.
  20. Minghui Ma & Oguzhan Pektezel & Vincenzo Ballerini & Paolo Valdiserri & Eugenia Rossi di Schio, 2024. "Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods," Energies, MDPI, vol. 17(22), pages 1-16, November.
  21. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
  22. Pan, Yue & Zhang, Limao, 2020. "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, Elsevier, vol. 268(C).
  23. 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.
  24. Löhr, Yannik & Wolf, Daniel & Pollerberg, Clemens & Hörsting, Alexander & Mönnigmann, Martin, 2021. "Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages," Applied Energy, Elsevier, vol. 290(C).
  25. 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.
  26. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
  27. 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).
  28. Li, Xinyue & Chen, Shuqin & Li, Hongliang & Lou, Yunxiao & Li, Jiahe, 2023. "A behavior-orientated prediction method for short-term energy consumption of air-conditioning systems in buildings blocks," Energy, Elsevier, vol. 263(PD).
  29. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  30. 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.
  31. Zhou, Yuekuan & Zheng, Siqian, 2020. "Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities," Applied Energy, Elsevier, vol. 262(C).
  32. 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.
  33. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
  34. Thomas Wu & Bo Wang & Dongdong Zhang & Ziwei Zhao & Hongyu Zhu, 2023. "Benchmarking Evaluation of Building Energy Consumption Based on Data Mining," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
  35. Sun, Chunhua & Yan, Hao & Yuan, Lingyu & Cao, Shanshan & Ma, Weichi & Suo, Chenyu & Qi, Chengying & Wu, Xiangdong, 2024. "A refined classification method of heat customers to improve inter-household thermal balance intelligent control and regulation," Energy, Elsevier, vol. 304(C).
  36. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
  37. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
  38. Zhang, Chengyu & Ma, Liangdong & Han, Xing & Zhao, Tianyi, 2024. "Reconstituted data-driven air conditioning energy consumption prediction system employing occupant-orientated probability model as input and swarm intelligence optimization algorithms," Energy, Elsevier, vol. 288(C).
  39. Li, Jie & Suvarna, Manu & Pan, Lanjia & Zhao, Yingru & Wang, Xiaonan, 2021. "A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification," Applied Energy, Elsevier, vol. 304(C).
  40. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
  41. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
  42. Samira Rastbod & Farnaz Rahimi & Yara Dehghan & Saeed Kamranfar & Omrane Benjeddou & Moncef L. Nehdi, 2022. "An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
  43. Wang, Yanmin & Li, Zhiwei & Liu, Junjie & Pei, Mingzhe & Zhao, Yan & Lu, Xuan, 2023. "Data-driven analysis and prediction of indoor characteristic temperature in district heating systems," Energy, Elsevier, vol. 282(C).
  44. Sun, Chunhua & Liu, Yiting & Cao, Shanshan & Chen, Jiali & Xia, Guoqiang & Wu, Xiangdong, 2022. "Identification of control regularity of heating stations based on cross-correlation function dynamic time delay method," Energy, Elsevier, vol. 246(C).
  45. Zhang, Xiaokong & Chai, Jian & Tian, Lingyue & Yang, Ying & Zhang, Zhe George & Pan, Yue, 2023. "Forecast and structural characteristics of China's oil product consumption embedded in bottom-line thinking," Energy, Elsevier, vol. 278(PA).
  46. Wu, Xianguo & Li, Xinyi & Qin, Yawei & Xu, Wen & Liu, Yang, 2023. "Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions," Applied Energy, Elsevier, vol. 339(C).
  47. Wang, Yongjie & Zhan, Changhong & Li, Guanghao & Ren, Shaochen, 2024. "Comparison of algorithms for heat load prediction of buildings," Energy, Elsevier, vol. 297(C).
  48. Antonio Manuel Gómez-Orellana & Juan Carlos Fernández & Manuel Dorado-Moreno & Pedro Antonio Gutiérrez & César Hervás-Martínez, 2021. "Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux," Energies, MDPI, vol. 14(2), pages 1-33, January.
  49. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
  50. 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).
  51. Zhang, Weiyi & Zhou, Haiyang & Bao, Xiaohua & Cui, Hongzhi, 2023. "Outlet water temperature prediction of energy pile based on spatial-temporal feature extraction through CNN–LSTM hybrid model," Energy, Elsevier, vol. 264(C).
  52. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
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