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Machine Learning for Energy Systems Optimization

Author

Listed:
  • Insu Kim

    (Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea)

  • Beopsoo Kim

    (Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea)

  • Denis Sidorov

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

Abstract

This editorial overviews the contents of the Special Issue “Machine Learning for Energy Systems 2021” and review the trends in machine learning (ML) techniques for energy system (ES) optimization [...]

Suggested Citation

  • Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4116-:d:831132
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    References listed on IDEAS

    as
    1. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Reza Arghandeh & Jeremy Woyak & Ahmet Onen & Jaesung Jung & Robert P. Broadwater, 2014. "Economic Optimal Operation of Community Energy Storage Systems in Competitive Energy Markets," Papers 1407.0433, arXiv.org, revised Sep 2014.
    3. Raji Atia & Noboru Yamada, 2016. "Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids," Energies, MDPI, vol. 9(3), pages 1-16, March.
    4. Bastien Politi & Alain Foucaran & Nicolas Camara, 2022. "Low-Cost Sensors for Indoor PV Energy Harvesting Estimation Based on Machine Learning," Energies, MDPI, vol. 15(3), pages 1-16, February.
    5. Hai Guo & Qun Ding & Yifan Song & Haoran Tang & Likun Wang & Jingying Zhao, 2020. "Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network," Energies, MDPI, vol. 13(18), pages 1-14, September.
    6. Kim, Insu, 2018. "Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method," Energy, Elsevier, vol. 150(C), pages 642-652.
    7. Olga Melnikova & Alexandr Nazarychev & Konstantin Suslov, 2022. "Enhancement of the Technique for Calculation and Assessment of the Condition of Major Insulation of Power Transformers," Energies, MDPI, vol. 15(4), pages 1-13, February.
    8. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    9. Dini, Anoosh & Hassankashi, Alireza & Pirouzi, Sasan & Lehtonen, Matti & Arandian, Behdad & Baziar, Ali Asghar, 2022. "A flexible-reliable operation optimization model of the networked energy hubs with distributed generations, energy storage systems and demand response," Energy, Elsevier, vol. 239(PA).
    10. Yu Zhang & Xiaohui Song & Yong Li & Zilong Zeng & Chenchen Yong & Denis Sidorov & Xia Lv, 2020. "Two-Stage Active and Reactive Power Coordinated Optimal Dispatch for Active Distribution Network Considering Load Flexibility," Energies, MDPI, vol. 13(22), pages 1-13, November.
    11. Haesung Jo & Jaemin Park & Insu Kim, 2021. "Environmentally Constrained Optimal Dispatch Method for Combined Cooling, Heating, and Power Systems Using Two-Stage Optimization," Energies, MDPI, vol. 14(14), pages 1-20, July.
    12. 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).
    13. Soyeong Park & Seungwook Yoon & Byungtak Lee & Seokkap Ko & Euiseok Hwang, 2020. "Probabilistic Forecasting Based Joint Detection and Imputation of Clustered Bad Data in Residential Electricity Loads," Energies, MDPI, vol. 14(1), pages 1-13, December.
    14. Ethelbert Ezemobi & Andrea Tonoli & Mario Silvagni, 2021. "Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine," Energies, MDPI, vol. 14(8), pages 1-15, April.
    15. Arghandeh, Reza & Woyak, Jeremy & Onen, Ahmet & Jung, Jaesung & Broadwater, Robert P., 2014. "Economic optimal operation of Community Energy Storage systems in competitive energy markets," Applied Energy, Elsevier, vol. 135(C), pages 71-80.
    16. Howlader, Harun Or Rashid & Matayoshi, Hidehito & Senjyu, Tomonobu, 2016. "Distributed generation integrated with thermal unit commitment considering demand response for energy storage optimization of smart grid," Renewable Energy, Elsevier, vol. 99(C), pages 107-117.
    17. Ilia Shushpanov & Konstantin Suslov & Pavel Ilyushin & Denis N. Sidorov, 2021. "Towards the Flexible Distribution Networks Design Using the Reliability Performance Metric," Energies, MDPI, vol. 14(19), pages 1-24, September.
    18. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    19. Beopsoo Kim & Nikita Rusetskii & Haesung Jo & Insu Kim, 2021. "The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method," Energies, MDPI, vol. 14(2), pages 1-18, January.
    20. Donghyeon Lee & Seungwan Son & Insu Kim, 2021. "Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization," Energies, MDPI, vol. 14(11), pages 1-19, May.
    21. Leerbeck, Kenneth & Bacher, Peder & Junker, Rune Grønborg & Goranović, Goran & Corradi, Olivier & Ebrahimy, Razgar & Tveit, Anna & Madsen, Henrik, 2020. "Short-term forecasting of CO2 emission intensity in power grids by machine learning," Applied Energy, Elsevier, vol. 277(C).
    22. Nikolai Voropai, 2020. "Electric Power System Transformations: A Review of Main Prospects and Challenges," Energies, MDPI, vol. 13(21), pages 1-16, October.
    23. Jaemin Park & Haesung Jo & Insu Kim, 2021. "The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers," Energies, MDPI, vol. 14(18), pages 1-25, September.
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    Cited by:

    1. Kai Xu & Youguang Guo & Gang Lei & Jianguo Zhu, 2023. "A Review of Flywheel Energy Storage System Technologies," Energies, MDPI, vol. 16(18), pages 1-32, September.
    2. Ivan Postnikov & Ekaterina Samarkina & Andrey Penkovskii & Vladimir Kornev & Denis Sidorov, 2023. "Modeling Unpredictable Behavior of Energy Facilities to Ensure Reliable Operation in a Cyber-Physical System," Energies, MDPI, vol. 16(19), pages 1-11, October.
    3. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.

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