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Machine Learning for Energy Systems

Author

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  • Denis Sidorov

    (Applied Mathematics Department, Energy Systems Institute, Siberian Branch of Russian Academy of Sciences, 664033 Irkutsk, Russia
    Industrial Mathematics Laboratory, Baikal School of BRICS, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Fang Liu

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

  • Yonghui Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

Abstract

The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling.

Suggested Citation

  • Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4708-:d:411341
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    References listed on IDEAS

    as
    1. Hua Liu & Yong Li & Yijia Cao & Zilong Zeng & Denis Sidorov, 2020. "Operational Risk Assessment of Electric-Gas Integrated Energy Systems Considering N-1 Accidents," Energies, MDPI, vol. 13(5), pages 1-16, March.
    2. Zilong Zeng & Yong Li & Yijia Cao & Yirui Zhao & Junjie Zhong & Denis Sidorov & Xiangcheng Zeng, 2020. "Blockchain Technology for Information Security of the Energy Internet: Fundamentals, Features, Strategy and Application," Energies, MDPI, vol. 13(4), pages 1-24, February.
    3. Senhui Wang & Haifeng Li & Yongjie Zhang & Zongshu Zou, 2019. "An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making," Energies, MDPI, vol. 12(18), pages 1-15, September.
    4. Ruixuan Yang & Fulin Zhou & Kai Zhong, 2020. "A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism," Energies, MDPI, vol. 13(8), pages 1-15, April.
    5. Syed Naeem Haider & Qianchuan Zhao & Xueliang Li, 2020. "Cluster-Based Prediction for Batteries in Data Centers," Energies, MDPI, vol. 13(5), pages 1-17, March.
    6. Rongyong Zhao & Daheng Dong & Cuiling Li & Steven Liu & Hao Zhang & Miyuan Li & Wenzhong Shen, 2020. "An Improved Power Control Approach for Wind Turbine Fatigue Balancing in an Offshore Wind Farm," Energies, MDPI, vol. 13(7), pages 1-20, March.
    7. Fulin Zhou & Feifan Liu & Ruixuan Yang & Huanrui Liu, 2020. "Method for Estimating Harmonic Parameters Based on Measurement Data without Phase Angle," Energies, MDPI, vol. 13(4), pages 1-19, February.
    8. Denis Sidorov & Daniil Panasetsky & Nikita Tomin & Dmitriy Karamov & Aleksei Zhukov & Ildar Muftahov & Aliona Dreglea & Fang Liu & Yong Li, 2020. "Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region," Energies, MDPI, vol. 13(5), pages 1-18, March.
    9. Gangjun Gong & Zhening Zhang & Xinyu Zhang & Nawaraj Kumar Mahato & Lin Liu & Chang Su & Haixia Yang, 2020. "Electric Power System Operation Mechanism with Energy Routers Based on QoS Index under Blockchain Architecture," Energies, MDPI, vol. 13(2), pages 1-22, January.
    10. Ahmad Nayyar Hassan & Ayman El-Hag, 2020. "Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction," Energies, MDPI, vol. 13(7), pages 1-11, April.
    11. Fang Liu & Ranran Li & Aliona Dreglea, 2019. "Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model," Energies, MDPI, vol. 12(18), pages 1-16, September.
    12. Stéfano Frizzo Stefenon & Roberto Zanetti Freire & Leandro dos Santos Coelho & Luiz Henrique Meyer & Rafael Bartnik Grebogi & William Gouvêa Buratto & Ademir Nied, 2020. "Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System," Energies, MDPI, vol. 13(2), pages 1-19, January.
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    Cited by:

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    2. Xiaotong Qie & Rui Zhang & Yanyong Hu & Xialing Sun & Xue Chen, 2021. "A Multi-Criteria Decision-Making Approach for Energy Storage Technology Selection Based on Demand," Energies, MDPI, vol. 14(20), pages 1-29, October.
    3. Hana Charvátová & Aleš Procházka & Martin Zálešák, 2020. "Computer Simulation of Passive Cooling of Wooden House Covered by Phase Change Material," Energies, MDPI, vol. 13(22), pages 1-15, November.
    4. Songyao Wang & Zhisheng Zhang, 2021. "Short-Term Multiple Load Forecasting Model of Regional Integrated Energy System Based on QWGRU-MTL," Energies, MDPI, vol. 14(20), pages 1-13, October.
    5. Prabha Bhola & Alexandros-Georgios Chronis & Panos Kotsampopoulos & Nikos Hatziargyriou, 2023. "Business Model Selection for Community Energy Storage: A Multi Criteria Decision Making Approach," Energies, MDPI, vol. 16(18), pages 1-30, September.
    6. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    7. Mukhamet, Tileuzhan & Kobeyev, Sultan & Nadeem, Abid & Memon, Shazim Ali, 2021. "Ranking PCMs for building façade applications using multi-criteria decision-making tools combined with energy simulations," Energy, Elsevier, vol. 215(PB).
    8. Zhou, Yuekuan & Zheng, Siqian, 2020. "Multi-level uncertainty optimisation on phase change materials integrated renewable systems with hybrid ventilations and active cooling," Energy, Elsevier, vol. 202(C).
    9. Abokersh, Mohamed Hany & Gangwar, Sachin & Spiekman, Marleen & Vallès, Manel & Jiménez, Laureano & Boer, Dieter, 2021. "Sustainability insights on emerging solar district heating technologies to boost the nearly zero energy building concept," Renewable Energy, Elsevier, vol. 180(C), pages 893-913.
    10. Amaral, C. & Silva, T. & Mohseni, F. & Amaral, J.S. & Amaral, V.S. & Marques, P.A.A.P. & Barros-Timmons, A. & Vicente, R., 2021. "Experimental and numerical analysis of the thermal performance of polyurethane foams panels incorporating phase change material," Energy, Elsevier, vol. 216(C).
    11. Ding, Zhixiong & Wu, Wei & Leung, Michael, 2021. "Advanced/hybrid thermal energy storage technology: material, cycle, system and perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    12. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.
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