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Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review

Author

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  • Mario Pérez-Gomariz

    (Department of Information Technologies and Telecommunications, ETSIT—UPCT, Antiguo Cuartel de Antigones, Plaza del Hospital 1, 30202 Cartagena, Spain)

  • Antonio López-Gómez

    (Department of Agricultural Engineering, ETSIA—UPCT, Paseo Alfonso XIII 48, 30203 Cartagena, Spain)

  • Fernando Cerdán-Cartagena

    (Department of Information Technologies and Telecommunications, ETSIT—UPCT, Antiguo Cuartel de Antigones, Plaza del Hospital 1, 30202 Cartagena, Spain)

Abstract

The refrigeration industry is an energy-intensive sector. Increasing the efficiency of industrial refrigeration systems is crucial for reducing production costs and minimizing CO 2 emissions. Optimization of refrigeration systems is often a complex and time-consuming problem. This is where technologies such as big data and artificial intelligence play an important role. Nowadays, smart sensorization and the development of IoT (Internet of Things) make the massive connection of all kinds of devices possible, thereby enabling a new way of data acquisition. In this scenario, refrigeration systems can be measured comprehensively by acquiring large volumes of data in real-time. Then, artificial neural network (ANN) models can use the data to drive autonomous decision-making to build more efficient refrigeration systems.

Suggested Citation

  • Mario Pérez-Gomariz & Antonio López-Gómez & Fernando Cerdán-Cartagena, 2023. "Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review," Clean Technol., MDPI, vol. 5(1), pages 1-21, January.
    • Handle: RePEc:gam:jcltec:v:5:y:2023:i:1:p:7-136:d:1032635
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    References listed on IDEAS

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    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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).
    9. 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.
    10. 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).
    11. 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.
    12. 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).
    13. 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.
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