Transfer Learning Prediction Performance of Chillers for Neural Network Models
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Coraci, Davide & Brandi, Silvio & Hong, Tianzhen & Capozzoli, Alfonso, 2023. "Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings," Applied Energy, Elsevier, vol. 333(C).
- 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.
- Qian, Fanyue & Gao, Weijun & Yang, Yongwen & Yu, Dan, 2020. "Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption," Energy, Elsevier, vol. 193(C).
- 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).
- 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.
- Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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).
- 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).
- 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).
- Lu, Yakai & Tian, Zhe & Zhang, Qiang & Zhou, Ruoyu & Chu, Chengshan, 2021. "Data augmentation strategy for short-term heating load prediction model of residential building," Energy, Elsevier, vol. 235(C).
- Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
- 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).
- Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).
- Fan, Cheng & Lei, Yutian & Sun, Yongjun & Piscitelli, Marco Savino & Chiosa, Roberto & Capozzoli, Alfonso, 2022. "Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context," Energy, Elsevier, vol. 240(C).
- 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).
- Li, Shuangqi & He, Hongwen & Li, Jianwei, 2019. "Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology," Applied Energy, Elsevier, vol. 242(C), pages 1259-1273.
- Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
- Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
- Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
- Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
- Nweye, Kingsley & Sankaranarayanan, Siva & Nagy, Zoltan, 2023. "MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities," Applied Energy, Elsevier, vol. 346(C).
- Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).
- Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
- Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
- Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
- Yuhong Zhao & Ruirui Liu & Zhansheng Liu & Liang Liu & Jingjing Wang & Wenxiang Liu, 2023. "A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
More about this item
Keywords
building automation; transfer learning; deep neural network; prediction; chiller; measurement;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7149-:d:1262887. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.