IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i12p4427-d841503.html
   My bibliography  Save this article

Review on Interpretable Machine Learning in Smart Grid

Author

Listed:
  • Chongchong Xu

    (School of Automation, Central South University, Changsha 410083, China)

  • Zhicheng Liao

    (School of Automation, Central South University, Changsha 410083, China)

  • Chaojie Li

    (Department of Electrical Engineering and Telecommunications, University of New South Wales, Kensington, NSW 2052, Australia)

  • Xiaojun Zhou

    (School of Automation, Central South University, Changsha 410083, China)

  • Renyou Xie

    (Department of Electrical Engineering and Telecommunications, University of New South Wales, Kensington, NSW 2052, Australia)

Abstract

In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.

Suggested Citation

  • Chongchong Xu & Zhicheng Liao & Chaojie Li & Xiaojun Zhou & Renyou Xie, 2022. "Review on Interpretable Machine Learning in Smart Grid," Energies, MDPI, vol. 15(12), pages 1-31, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4427-:d:841503
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/12/4427/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/12/4427/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    3. Luo, Xing & Dooner, Mark & He, Wei & Wang, Jihong & Li, Yaowang & Li, Decai & Kiselychnyk, Oleh, 2018. "Feasibility study of a simulation software tool development for dynamic modelling and transient control of adiabatic compressed air energy storage with its electrical power system applications," Applied Energy, Elsevier, vol. 228(C), pages 1198-1219.
    4. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).
    5. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
    6. Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
    7. Li, Ao & Xiao, Fu & Zhang, Chong & Fan, Cheng, 2021. "Attention-based interpretable neural network for building cooling load prediction," Applied Energy, Elsevier, vol. 299(C).
    8. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    9. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    10. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    11. Chengcheng Gu & Hua Li, 2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy," Energies, MDPI, vol. 15(4), pages 1-19, February.
    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. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    2. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
    3. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Ghulam Moeen Uddin & Waqar Muhammad Ashraf & Karolina Grabowska & Anna Zylka & Anna Kulakowska & Wojciech Nowak, 2023. "Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives," Energies, MDPI, vol. 16(8), pages 1-12, April.
    4. Aya Amer & Khaled Shaban & Ahmed Massoud, 2022. "Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes," Energies, MDPI, vol. 15(21), pages 1-20, November.
    5. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    6. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.

    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. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
    2. Lan, Puzhe & Han, Dong & Xu, Xiaoyuan & Yan, Zheng & Ren, Xijun & Xia, Shiwei, 2022. "Data-driven state estimation of integrated electric-gas energy system," Energy, Elsevier, vol. 252(C).
    3. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    4. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    5. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    6. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    7. Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
    8. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    9. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    10. 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).
    11. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
    12. 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.
    13. 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).
    14. Arman Goudarzi & Farzad Ghayoor & Muhammad Waseem & Shah Fahad & Issa Traore, 2022. "A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook," Energies, MDPI, vol. 15(19), pages 1-32, September.
    15. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
    16. Huang, Wanjun & Zhang, Xinran & Zheng, Weiye, 2021. "Resilient power network structure for stable operation of energy systems: A transfer learning approach," Applied Energy, Elsevier, vol. 296(C).
    17. Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).
    18. Varlamis, Iraklis & Sardianos, Christos & Chronis, Christos & Dimitrakopoulos, George & Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2022. "Smart fusion of sensor data and human feedback for personalized energy-saving recommendations," Applied Energy, Elsevier, vol. 305(C).
    19. Noman, Abdullah Al & Tasneem, Zinat & Sahed, Md. Fahad & Muyeen, S.M. & Das, Sajal K. & Alam, Firoz, 2022. "Towards next generation Savonius wind turbine: Artificial intelligence in blade design trends and framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    20. Fath U Min Ullah & Noman Khan & Tanveer Hussain & Mi Young Lee & Sung Wook Baik, 2021. "Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework," Mathematics, MDPI, vol. 9(6), pages 1-22, March.

    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:15:y:2022:i:12:p:4427-:d:841503. 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.