IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v148y2018icp269-282.html

Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Guixiang Xue & Yu Pan & Tao Lin & Jiancai Song & Chengying Qi & Zhipan Wang, 2019. "District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model," Energies, MDPI, vol. 12(11), pages 1-21, June.
  2. Liu, Xuefeng & Huang, Bin & Zheng, Yulan, 2023. "Control strategy for dynamic operation of multiple chillers under random load constraints," Energy, Elsevier, vol. 270(C).
  3. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry," Energy, Elsevier, vol. 162(C), pages 593-602.
  4. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Namje Park, 2020. "Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting," Energies, MDPI, vol. 13(11), pages 1-23, May.
  5. 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.
  6. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  7. 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).
  8. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
  9. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
  10. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Li, Wenqiang & Peng, Pei, 2021. "A hybrid deep transfer learning strategy for short term cross-building energy prediction," Energy, Elsevier, vol. 215(PB).
  11. 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).
  12. Xiong, Yongkang & Zeng, Zhenfeng & Xin, Jianbo & Song, Guanhong & Xia, Yonghong & Xu, Zaide, 2023. "Renewable energy time series regulation strategy considering grid flexible load and N-1 faults," Energy, Elsevier, vol. 284(C).
  13. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho & Acharya, Rajendra & Dinh, Toan, 2025. "Electricity demand uncertainty modeling with Temporal Convolution Neural Network models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 209(C).
  14. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2024. "Real-time automatic control of multi-energy system for smart district community: A coupling ensemble prediction model and safe deep reinforcement learning," Energy, Elsevier, vol. 304(C).
  15. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
  16. 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.
  17. Liu, Hui & Yu, Chengqing & Wu, Haiping & Duan, Zhu & Yan, Guangxi, 2020. "A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting," Energy, Elsevier, vol. 202(C).
  18. Li, Guozhu & Ding, Chenjun & Zhao, Naini & Wei, Jiaxing & Guo, Yang & Meng, Chong & Huang, Kailiang & Zhu, Rongxin, 2024. "Research on a novel photovoltaic power forecasting model based on parallel long and short-term time series network," Energy, Elsevier, vol. 293(C).
  19. Zhang, Guoqiang & Guo, Jifeng, 2020. "A novel ensemble method for residential electricity demand forecasting based on a novel sample simulation strategy," Energy, Elsevier, vol. 207(C).
  20. 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.
  21. Zhang, Guoqiang & Guo, Jifeng, 2020. "A novel ensemble method for hourly residential electricity consumption forecasting by imaging time series," Energy, Elsevier, vol. 203(C).
  22. Chen, Zhiwen & Deng, Qiao & Ren, Hao & Zhao, Zhengrun & Peng, Tao & Yang, Chunhua & Gui, Weihua, 2022. "A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data," Applied Energy, Elsevier, vol. 310(C).
  23. Sun, Jian & Liu, Gang & Sun, Boyang & Xiao, Gang, 2021. "Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection," Applied Energy, Elsevier, vol. 303(C).
  24. Yun Duan, 2022. "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
  25. Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
  26. Zhu, Jianhua & He, Yaoyao, 2025. "A novel hybrid model based on evolving multi-quantile long and short-term memory neural network for ultra-short-term probabilistic forecasting of photovoltaic power," Applied Energy, Elsevier, vol. 377(PC).
  27. Jiubo Zhang & Bowen Zhou & Zhile Yang & Yuanjun Guo & Chen Lv & Xiaofeng Xu & Jichun Liu, 2025. "A Review of Industrial Load Flexibility Enhancement for Demand-Response Interaction," Sustainability, MDPI, vol. 17(11), pages 1-31, May.
  28. Ridha, Hussein Mohammed & Ahmadipour, Masoud & Alghrairi, Mokhalad & Hizam, Hashim & Mirjalili, Seyedali & Zubaidi, Salah L. & Mohammed S, Marwa Y., 2026. "A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive beluga whale optimization, and improved extreme learning machine," Renewable Energy, Elsevier, vol. 256(PA).
  29. 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.
  30. Hadavimoghaddam, Fahimeh & Amiri-Ramsheh, Behnam & Atashrouz, Saeid & Abedi, Ali & Mohaddespour, Ahmad & Ostadhassan, Mehdi & Hemmati-Sarapardeh, Abdolhossein, 2024. "Modeling CO2 loading capacity of triethanolamine (TEA) aqueous solutions via a deep learning approach," Energy, Elsevier, vol. 313(C).
  31. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
  32. Ma, Yichuan X. & Yeung, Lawrence K., 2024. "BEForeGAN: An image-based deep generative approach for day-ahead forecasting of building HVAC energy consumption," Applied Energy, Elsevier, vol. 376(PA).
  33. 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.
  34. Hao, Xiaochen & Guo, Tongtong & Huang, Gaolu & Shi, Xin & Zhao, Yantao & Yang, Yue, 2020. "Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window," Energy, Elsevier, vol. 207(C).
  35. Li, Wenqiang & Gong, Guangcai & Fan, Houhua & Peng, Pei & Chun, Liang, 2020. "Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting," Applied Energy, Elsevier, vol. 270(C).
  36. Cai, Jianyang & Yang, Haidong & Song, Cairong & Xu, Kangkang, 2024. "A novel graph convolutional network-based interpretable method for chiller energy consumption prediction considering the spatiotemporal coupling between variables," Energy, Elsevier, vol. 312(C).
  37. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.