IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i10p4025-d1144365.html
   My bibliography  Save this article

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

Author

Listed:
  • Wadim Strielkowski

    (Department of Trade and Finance, Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamýcká 129, Prague 6, 165 00 Prague, Czech Republic)

  • Andrey Vlasov

    (Department of Design and Technology of Electronic Devices, Bauman Moscow State Technical University, 2-aj Baumanskaj str. 5/1, 105005 Moscow, Russia)

  • Kirill Selivanov

    (Department of Design and Technology of Electronic Devices, Bauman Moscow State Technical University, 2-aj Baumanskaj str. 5/1, 105005 Moscow, Russia)

  • Konstantin Muraviev

    (Department of Design and Technology of Electronic Devices, Bauman Moscow State Technical University, 2-aj Baumanskaj str. 5/1, 105005 Moscow, Russia)

  • Vadim Shakhnov

    (Department of Design and Technology of Electronic Devices, Bauman Moscow State Technical University, 2-aj Baumanskaj str. 5/1, 105005 Moscow, Russia)

Abstract

The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4025-:d:1144365
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/10/4025/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/10/4025/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrey I. Vlasov & Pavel V. Grigoriev & Aleksey I. Krivoshein & Vadim A. Shakhnov & Sergey S. Filin & Vladimir S. Migalin, 2018. "Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks," Post-Print hal-02342832, HAL.
    2. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    3. Dudjak, Viktorija & Neves, Diana & Alskaif, Tarek & Khadem, Shafi & Pena-Bello, Alejandro & Saggese, Pietro & Bowler, Benjamin & Andoni, Merlinda & Bertolini, Marina & Zhou, Yue & Lormeteau, Blanche &, 2021. "Impact of local energy markets integration in power systems layer: A comprehensive review," Applied Energy, Elsevier, vol. 301(C).
    4. Asaad, Mohammad & Ahmad, Furkan & Alam, Mohammad Saad & Sarfraz, Mohammad, 2021. "Smart grid and Indian experience: A review," Resources Policy, Elsevier, vol. 74(C).
    5. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    6. Elena Korneeva & Nina Olinder & Wadim Strielkowski, 2021. "Consumer Attitudes to the Smart Home Technologies and the Internet of Things (IoT)," Energies, MDPI, vol. 14(23), pages 1-15, November.
    7. 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).
    8. Mathias Uslar & Sebastian Rohjans & Christian Neureiter & Filip Pröstl Andrén & Jorge Velasquez & Cornelius Steinbrink & Venizelos Efthymiou & Gianluigi Migliavacca & Seppo Horsmanheimo & Helfried Bru, 2019. "Applying the Smart Grid Architecture Model for Designing and Validating System-of-Systems in the Power and Energy Domain: A European Perspective," Energies, MDPI, vol. 12(2), pages 1-40, January.
    9. Shitu Zhang & Zhixun Zhu & Yang Li, 2021. "A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges," Energies, MDPI, vol. 14(21), pages 1-13, November.
    10. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
    11. Georgios Fotis & Christos Dikeakos & Elias Zafeiropoulos & Stylianos Pappas & Vasiliki Vita, 2022. "Scalability and Replicability for Smart Grid Innovation Projects and the Improvement of Renewable Energy Sources Exploitation: The FLEXITRANSTORE Case," Energies, MDPI, vol. 15(13), pages 1-32, June.
    12. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    13. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    14. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    15. Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.
    16. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    17. 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).
    18. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    19. Wadim Strielkowski & Tatyana Veinbender & Manuela Tvaronavičienė & Natalja Lace, 2020. "Economic efficiency and energy security of smart cities," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 788-803, January.
    20. Wafa Nafkha-Tayari & Seifeddine Ben Elghali & Ehsan Heydarian-Forushani & Mohamed Benbouzid, 2022. "Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects," Energies, MDPI, vol. 15(10), pages 1-20, May.
    21. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    22. Andrey I. Vlasov & Boris V. Artemiev & Kirill V. Selivanov & Kirill S. Mironov & Jasur O. Isroilov, 2022. "Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 1-7, May.
    23. 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).
    24. Huang, Pei & Copertaro, Benedetta & Zhang, Xingxing & Shen, Jingchun & Löfgren, Isabelle & Rönnelid, Mats & Fahlen, Jan & Andersson, Dan & Svanfeldt, Mikael, 2020. "A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating," Applied Energy, Elsevier, vol. 258(C).
    25. Georgi N. Todorov & Andrey I. Vlasov & Elena E. Volkova & Marina A. Osintseva, 2020. "Sustainability in Local Power Supply Systems of Production Facilities Where There Is the Compensatory Use of Renewable Energy Sources," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 14-23.
    26. Abdellatif Elmouatamid & Radouane Ouladsine & Mohamed Bakhouya & Najib El Kamoun & Mohammed Khaidar & Khalid Zine-Dine, 2020. "Review of Control and Energy Management Approaches in Micro-Grid Systems," Energies, MDPI, vol. 14(1), pages 1-30, December.
    27. Jihoon Moon & Sungwoo Park & Seungmin Rho & Eenjun Hwang, 2019. "A comparative analysis of artificial neural network architectures for building energy consumption forecasting," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    28. Evgeny Lisin & Daria Shuvalova & Irina Volkova & Wadim Strielkowski, 2018. "Sustainable Development of Regional Power Systems and the Consumption of Electric Energy," Sustainability, MDPI, vol. 10(4), pages 1-10, April.
    29. Shahid Tufail & Imtiaz Parvez & Shanzeh Batool & Arif Sarwat, 2021. "A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid," Energies, MDPI, vol. 14(18), pages 1-22, September.
    30. Tehseen Mazhar & Hafiz Muhammad Irfan & Sunawar Khan & Inayatul Haq & Inam Ullah & Muhammad Iqbal & Habib Hamam, 2023. "Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods," Future Internet, MDPI, vol. 15(2), pages 1-37, February.
    31. May McMaster & Charlie Nettleton & Christeen Tom & Belanda Xu & Cheng Cao & Ping Qiao, 2020. "Risk Management: Rethinking Fashion Supply Chain Management for Multinational Corporations in Light of the COVID-19 Outbreak," JRFM, MDPI, vol. 13(8), pages 1-16, August.
    32. Sajjad Ali & Imran Khan & Sadaqat Jan & Ghulam Hafeez, 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid," Energies, MDPI, vol. 14(8), pages 1-29, April.
    33. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    34. Daiva Stanelyte & Neringa Radziukyniene & Virginijus Radziukynas, 2022. "Overview of Demand-Response Services: A Review," Energies, MDPI, vol. 15(5), pages 1-31, February.
    35. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    36. Andrey I. Vlasov & Vadim A. Shakhnov & Sergey S. Filin & Sergey S. Filin & Aleksey I. Krivoshein & Aleksey I. Krivoshein, 2019. "Sustainable energy systems in the digital economy: concept of smart machines," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(4), pages 1975-1986, June.
    37. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    38. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    39. Paul, Sanjoy Kumar & Chowdhury, Priyabrata & Moktadir, Md. Abdul & Lau, Kwok Hung, 2021. "Supply chain recovery challenges in the wake of COVID-19 pandemic," Journal of Business Research, Elsevier, vol. 136(C), pages 316-329.
    40. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    41. Gordon Rausser & Wadim Strielkowski & Dalia Å treimikienÄ—, 2018. "Smart meters and household electricity consumption: A case study in Ireland," Energy & Environment, , vol. 29(1), pages 131-146, February.
    42. Georgi N. Todorov & Elena E. Volkova & Andrey I. Vlasov & Natalya I. Nikitina, 2019. "Modeling Energy-Efficient Consumption at Industrial Enterprises," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 10-18.
    43. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
    44. Yap, Kah Yung & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2022. "Future outlook on 6G technology for renewable energy sources (RES)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    45. Abdellah Chehri & Issouf Fofana & Xiaomin Yang, 2021. "Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    46. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    47. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    48. Mingming Wen & Changshi Zhou & Mamonov Konstantin, 2023. "Deep Neural Network for Predicting Changing Market Demands in the Energy Sector for a Sustainable Economy," Energies, MDPI, vol. 16(5), pages 1-17, March.
    49. Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron, 2019. "Are complex energy system models more accurate? An intra-model comparison of power system optimization models," Applied Energy, Elsevier, vol. 255(C).
    50. Aqdas Naz & Muhammad Umar Javed & Nadeem Javaid & Tanzila Saba & Musaed Alhussein & Khursheed Aurangzeb, 2019. "Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids," Energies, MDPI, vol. 12(5), pages 1-30, March.
    51. Andrey I. Vlasov & Pavel V. Grigoriev & Aleksey I. Krivoshein & Aleksey I. Krivoshein & Vadim A. Shakhnov & Sergey S. Filin & Sergey S. Filin & Vladimir S. Migalin, 2018. "Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(2), pages 489-502, December.
    52. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.
    53. Gabriel Koman & Oliver Bubelíny & Dominika Tumová & Radoslav Jankal, 2022. "Sustainable transport within the context of smart cities in the Slovak republic," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 10(1), pages 175-199, September.
    54. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    55. Ines Ortega-Fernandez & Francesco Liberati, 2023. "A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning," Energies, MDPI, vol. 16(2), pages 1-15, January.
    56. Turki Alsuwian & Aiman Shahid Butt & Arslan Ahmed Amin, 2022. "Smart Grid Cyber Security Enhancement: Challenges and Solutions—A Review," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
    57. Tehreem Nasir & Syed Sabir Hussain Bukhari & Safdar Raza & Hafiz Mudassir Munir & Muhammad Abrar & Hafiz Abd ul Muqeet & Kamran Liaquat Bhatti & Jong-Suk Ro & Rooha Masroor, 2021. "Recent Challenges and Methodologies in Smart Grid Demand Side Management: State-of-the-Art Literature Review," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-16, August.
    58. Benjamin Völker & Andreas Reinhardt & Anthony Faustine & Lucas Pereira, 2021. "Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective," Energies, MDPI, vol. 14(3), pages 1-21, January.
    59. Rizeakos, V. & Bachoumis, A. & Andriopoulos, N. & Birbas, M. & Birbas, A., 2023. "Deep learning-based application for fault location identification and type classification in active distribution grids," Applied Energy, Elsevier, vol. 338(C).
    60. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
    61. Arnob Das & Susmita Datta Peu & Md. Abdul Mannan Akanda & Abu Reza Md. Towfiqul Islam, 2023. "Peer-to-Peer Energy Trading Pricing Mechanisms: Towards a Comprehensive Analysis of Energy and Network Service Pricing (NSP) Mechanisms to Get Sustainable Enviro-Economical Energy Sector," Energies, MDPI, vol. 16(5), pages 1-27, February.
    62. Nguyen, Hai-Tra & Safder, Usman & Loy-Benitez, Jorge & Yoo, ChangKyoo, 2022. "Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy," Applied Energy, Elsevier, vol. 322(C).
    63. 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.
    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. Zhang, HongWei & Xie, Yuan, 2024. "Assessing natural resources, rebounding trends, digital economic structure and green recovery dynamics in China," Resources Policy, Elsevier, vol. 88(C).
    2. Wadim Strielkowski & Oxana Mukhoryanova & Oxana Kuznetsova & Yury Syrov, 2024. "Sustainable regional economic development and land use: a case of Russia," Papers 2404.12477, arXiv.org.
    3. Yensen Ni, 2024. "Navigating Energy and Financial Markets: A Review of Technical Analysis Used and Further Investigation from Various Perspectives," Energies, MDPI, vol. 17(12), pages 1-22, June.

    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. 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).
    2. Eren, Yavuz & Küçükdemiral, İbrahim, 2024. "A comprehensive review on deep learning approaches for short-term load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    3. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    4. Ajla Mehinovic & Matej Zajc & Nermin Suljanovic, 2023. "Interpretation and Quantification of the Flexibility Sources Location on the Flexibility Service in the Distribution Grid," Energies, MDPI, vol. 16(2), pages 1-18, January.
    5. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    6. Sheeraz Kirmani & Abdul Mazid & Irfan Ahmad Khan & Manaullah Abid, 2022. "A Survey on IoT-Enabled Smart Grids: Technologies, Architectures, Applications, and Challenges," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    7. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    8. Sandra Giraldo & David la Rotta & César Nieto-Londoño & Rafael E. Vásquez & Ana Escudero-Atehortúa, 2021. "Digital Transformation of Energy Companies: A Colombian Case Study," Energies, MDPI, vol. 14(9), pages 1-14, April.
    9. Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    10. Vinoth Kumar Ponnusamy & Padmanathan Kasinathan & Rajvikram Madurai Elavarasan & Vinoth Ramanathan & Ranjith Kumar Anandan & Umashankar Subramaniam & Aritra Ghosh & Eklas Hossain, 2021. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid," Sustainability, MDPI, vol. 13(23), pages 1-35, December.
    11. Elena Korneeva & Nina Olinder & Wadim Strielkowski, 2021. "Consumer Attitudes to the Smart Home Technologies and the Internet of Things (IoT)," Energies, MDPI, vol. 14(23), pages 1-15, November.
    12. Wadim Strielkowski & Olga Kovaleva & Tatiana Efimtseva, 2022. "Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    13. Muhammad Waseem & Muhammad Adnan Khan & Arman Goudarzi & Shah Fahad & Intisar Ali Sajjad & Pierluigi Siano, 2023. "Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges," Energies, MDPI, vol. 16(2), pages 1-29, January.
    14. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    15. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    16. Hafize Nurgul Durmus Senyapar & Ramazan Bayindir, 2023. "The Research Agenda on Smart Grids: Foresights for Social Acceptance," Energies, MDPI, vol. 16(18), pages 1-31, September.
    17. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.
    18. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    19. Diaa Salman & Mehmet Kusaf, 2021. "Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
    20. Beata Milewska & Dariusz Milewski, 2022. "Implications of Increasing Fuel Costs for Supply Chain Strategy," Energies, MDPI, vol. 15(19), pages 1-14, September.

    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:10:p:4025-:d:1144365. 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.