IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1688-d1061634.html
   My bibliography  Save this article

A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems

Author

Listed:
  • Jonas Sievers

    (Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany)

  • Thomas Blank

    (Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany)

Abstract

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-making process considering different objectives, scheduling algorithms, and implementations. Thus, based on our insights, we provide numerous recommendations for future research in residential and industrial energy management systems.

Suggested Citation

  • Jonas Sievers & Thomas Blank, 2023. "A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems," Energies, MDPI, vol. 16(4), pages 1-21, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1688-:d:1061634
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1688/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/4/1688/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu, Renzhi & Li, Yi-Chang & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2020. "Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management," Applied Energy, Elsevier, vol. 276(C).
    2. Yonghong Ma & Baixuan Li, 2020. "Hybridized Intelligent Home Renewable Energy Management System for Smart Grids," Sustainability, MDPI, vol. 12(5), pages 1-14, March.
    3. Nam-Kyu Kim & Myung-Hyun Shim & Dongjun Won, 2018. "Building Energy Management Strategy Using an HVAC System and Energy Storage System," Energies, MDPI, vol. 11(10), pages 1-15, October.
    4. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    5. Zafar Iqbal & Nadeem Javaid & Saleem Iqbal & Sheraz Aslam & Zahoor Ali Khan & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2018. "A Domestic Microgrid with Optimized Home Energy Management System," Energies, MDPI, vol. 11(4), pages 1-39, April.
    6. Bishwajit Dey & Fausto Pedro García Márquez & Sourav Kr. Basak, 2020. "Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm," Energies, MDPI, vol. 13(13), pages 1-23, July.
    7. Halmschlager, Verena & Hofmann, René, 2021. "Assessing the potential of combined production and energy management in Industrial Energy Hubs – Analysis of a chipboard production plant," Energy, Elsevier, vol. 226(C).
    8. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu, 2021. "Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data," Energies, MDPI, vol. 14(3), pages 1-18, February.
    9. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    10. Victor J. Gutierrez-Martinez & Carlos A. Moreno-Bautista & Jose M. Lozano-Garcia & Alejandro Pizano-Martinez & Enrique A. Zamora-Cardenas & Miguel A. Gomez-Martinez, 2019. "A Heuristic Home Electric Energy Management System Considering Renewable Energy Availability," Energies, MDPI, vol. 12(4), pages 1-20, February.
    11. Klyapovskiy, Sergey & Zheng, Yi & You, Shi & Bindner, Henrik W., 2021. "Optimal operation of the hydrogen-based energy management system with P2X demand response and ammonia plant," Applied Energy, Elsevier, vol. 304(C).
    12. Sadaqat Ali & Zhixue Zheng & Michel Aillerie & Jean-Paul Sawicki & Marie-Cécile Péra & Daniel Hissel, 2021. "A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications," Energies, MDPI, vol. 14(14), pages 1-26, July.
    13. Arshad Mohammad & Mohd Zuhaib & Imtiaz Ashraf & Marwan Alsultan & Shafiq Ahmad & Adil Sarwar & Mali Abdollahian, 2021. "Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability," Energies, MDPI, vol. 14(24), pages 1-27, December.
    14. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    15. Nikolaos Koltsaklis & Ioannis P. Panapakidis & David Pozo & Georgios C. Christoforidis, 2021. "A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques," Energies, MDPI, vol. 14(6), pages 1-32, March.
    16. Prasertsak Charoen & Nathavuth Kitbutrawat & Jasada Kudtongngam, 2022. "A Demand Response Implementation with Building Energy Management System," Energies, MDPI, vol. 15(3), pages 1-21, February.
    17. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    18. Bharath Varsh Rao & Friederich Kupzog & Martin Kozek, 2018. "Phase Balancing Home Energy Management System Using Model Predictive Control," Energies, MDPI, vol. 11(12), pages 1-19, November.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Arash Moghadasi, 2024. "Do SMEs Consider Open Data as a Vital Intellectual Asset? a Systematic Literature Review," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 11784-11818, September.

    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.
    1. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    2. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    3. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    4. Sameh Mahjoub & Larbi Chrifi-Alaoui & Saïd Drid & Nabil Derbel, 2023. "Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production," Energies, MDPI, vol. 16(4), pages 1-26, February.
    5. Nakıp, Mert & Çopur, Onur & Biyik, Emrah & Güzeliş, Cüneyt, 2023. "Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network," Applied Energy, Elsevier, vol. 340(C).
    6. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    7. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    8. 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).
    9. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    10. Senthil Prabu Ramalingam & Prabhakar Karthikeyan Shanmugam, 2022. "Hardware Implementation of a Home Energy Management System Using Remodeled Sperm Swarm Optimization (RMSSO) Algorithm," Energies, MDPI, vol. 15(14), pages 1-24, July.
    11. Li, Hongcheng & Yang, Dan & Cao, Huajun & Ge, Weiwei & Chen, Erheng & Wen, Xuanhao & Li, Chongbo, 2022. "Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system," Energy, Elsevier, vol. 239(PC).
    12. Xiaoyu Lin & Hang Yu & Meng Wang & Chaoen Li & Zi Wang & Yin Tang, 2021. "Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method," Energies, MDPI, vol. 14(16), pages 1-21, August.
    13. Tripathy, Prajukta & Jena, Pabitra Kumar & Mishra, Bikash Ranjan, 2024. "Systematic literature review and bibliometric analysis of energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    14. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    15. 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).
    16. Ayman A. Aly & Bassem F. Felemban & Ardashir Mohammadzadeh & Oscar Castillo & Andrzej Bartoszewicz, 2021. "Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis," Energies, MDPI, vol. 14(22), pages 1-21, November.
    17. M. Bilal Nasir & Asif Hussain & Kamran Ali Khan Niazi & Mashood Nasir, 2022. "An Optimal Energy Management System (EMS) for Residential and Industrial Microgrids," Energies, MDPI, vol. 15(17), pages 1-18, August.
    18. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    19. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    20. Abubakar Ahmad Musa & Adamu Hussaini & Weixian Liao & Fan Liang & Wei Yu, 2023. "Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey," Future Internet, MDPI, vol. 15(6), pages 1-24, May.

    Corrections

    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:4:p:1688-:d:1061634. 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.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.