Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Eom, Yong Hwan & Yoo, Jin Woo & Hong, Sung Bin & Kim, Min Soo, 2019. "Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving," Energy, Elsevier, vol. 187(C).
- 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).
- Elsa Chaerun Nisa & Yean-Der Kuan, 2021. "Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
- Zhao, Lei & Cai, Wenjian & Ding, Xudong & Chang, Weichung, 2013. "Model-based optimization for vapor compression refrigeration cycle," Energy, Elsevier, vol. 55(C), pages 392-402.
- Elsa Chaerun Nisa & Yean-Der Kuan & Chin-Chang Lai, 2021. "Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining," Energies, MDPI, vol. 14(20), pages 1-14, October.
- Dasheng Lee & Fu-Po Tsai, 2020. "Air Conditioning Energy Saving from Cloud-Based Artificial Intelligence: Case Study of a Split-Type Air Conditioner," Energies, MDPI, vol. 13(8), pages 1-25, April.
- 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.
- Rashidi, M.M. & Aghagoli, A. & Raoofi, R., 2017. "Thermodynamic analysis of the ejector refrigeration cycle using the artificial neural network," Energy, Elsevier, vol. 129(C), pages 201-215.
- Eom, Yong Hwan & Chung, Yoong & Park, Minsu & Hong, Sung Bin & Kim, Min Soo, 2021. "Deep learning-based prediction method on performance change of air source heat pump system under frosting conditions," Energy, Elsevier, vol. 228(C).
- 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).
- Krzysztof Wójcicki & Marta Biegańska & Beata Paliwoda & Justyna Górna, 2022. "Internet of Things in Industry: Research Profiling, Application, Challenges and Opportunities—A Review," Energies, MDPI, vol. 15(5), pages 1-24, February.
- 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.
- Maiorino, Angelo & Del Duca, Manuel Gesù & Aprea, Ciro, 2022. "ART.I.CO. (ARTificial Intelligence for COoling): An innovative method for optimizing the control of refrigeration systems based on Artificial Neural Networks," Applied Energy, Elsevier, vol. 306(PB).
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.- Li, Guannan & Chen, Liang & Liu, Jiangyan & Fang, Xi, 2023. "Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis," Energy, Elsevier, vol. 263(PD).
- Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
- 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.
- Ren, Zhengxiong & Han, Hua & Cui, Xiaoyu & Lu, Hailong & Luo, Mingwen, 2023. "Novel data-pulling-based strategy for chiller fault diagnosis in data-scarce scenarios," Energy, Elsevier, vol. 279(C).
- Bode, Gerrit & Thul, Simon & Baranski, Marc & Müller, Dirk, 2020. "Real-world application of machine-learning-based fault detection trained with experimental data," Energy, Elsevier, vol. 198(C).
- 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).
- Wang, Zhi & Peng, Xianyong & Zhou, Huaichun & Cao, Shengxian & Huang, Wenbo & Yan, Weijie & Li, Kuangyu & Fan, Siyuan, 2024. "A dynamic modeling method using channel-selection convolutional neural network: A case study of NOx emission," Energy, Elsevier, vol. 290(C).
- Luo, Zheng & Lin, Xiaojie & Qiu, Tianyue & Li, Manjie & Zhong, Wei & Zhu, Lingkai & Liu, Shuangcui, 2024. "Investigation of hybrid adversarial-diffusion sample generation method of substations in district heating system," Energy, Elsevier, vol. 288(C).
- Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
- Gao, Zhikun & Yang, Siyuan & Yu, Junqi & Zhao, Anjun, 2024. "Hybrid forecasting model of building cooling load based on combined neural network," Energy, Elsevier, vol. 297(C).
- Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
- Antonio Rosato & Francesco Guarino & Mohammad El Youssef & Alfonso Capozzoli & Massimiliano Masullo & Luigi Maffei, 2022. "Faulty Operation of Coils’ and Humidifier Valves in a Typical Air-Handling Unit: Experimental Impact Assessment of Indoor Comfort and Patterns of Operating Parameters under Mediterranean Climatic Cond," Energies, MDPI, vol. 15(18), pages 1-38, September.
- Liu, Jiejie & Li, Yao & Ma, Yanan & Qin, Ruomu & Meng, Xianyang & Wu, Jiangtao, 2023. "Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy," Energy, Elsevier, vol. 285(C).
- Shariq, M. Hasan & Hughes, Ben Richard, 2020. "Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
- Justyna Łapińska & Iwona Escher & Joanna Górka & Agata Sudolska & Paweł Brzustewicz, 2021. "Employees’ Trust in Artificial Intelligence in Companies: The Case of Energy and Chemical Industries in Poland," Energies, MDPI, vol. 14(7), pages 1-20, April.
- Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
- Janghorban Esfahani, Iman & Kang, Yong Tae & Yoo, ChangKyoo, 2014. "A high efficient combined multi-effect evaporation–absorption heat pump and vapor-compression refrigeration part 1: Energy and economic modeling and analysis," Energy, Elsevier, vol. 75(C), pages 312-326.
- Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
- 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).
- Ifaei, Pouya & Tayerani Charmchi, Amir Saman & Loy-Benitez, Jorge & Yang, Rebecca Jing & Yoo, ChangKyoo, 2022. "A data-driven analytical roadmap to a sustainable 2030 in South Korea based on optimal renewable microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
More about this item
Keywords
artificial intelligence; artificial neural networks; internet of things; energy saving; refrigeration systems; data-based models;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:jcltec:v:5:y:2023:i:1:p:7-136:d:1032635. 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.