IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v309y2022ics0306261921016834.html
   My bibliography  Save this article

Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19

Author

Listed:
  • Hu, Chenxi
  • Zhang, Jun
  • Yuan, Hongxia
  • Gao, Tianlu
  • Jiang, Huaiguang
  • Yan, Jing
  • Wenzhong Gao, David
  • Wang, Fei-Yue

Abstract

The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems.

Suggested Citation

  • Hu, Chenxi & Zhang, Jun & Yuan, Hongxia & Gao, Tianlu & Jiang, Huaiguang & Yan, Jing & Wenzhong Gao, David & Wang, Fei-Yue, 2022. "Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19," Applied Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016834
    DOI: 10.1016/j.apenergy.2021.118458
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921016834
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.118458?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Xiaoshun & Bao, Tao & Yu, Tao & Yang, Bo & Han, Chuanjia, 2017. "Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid," Energy, Elsevier, vol. 133(C), pages 348-365.
    2. Yan Gao & Yan Cui, 2020. "Author Correction: Deep transfer learning for reducing health care disparities arising from biomedical data inequality," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    3. Zhang, Xiaoshun & Chen, Yixuan & Yu, Tao & Yang, Bo & Qu, Kaiping & Mao, Senmao, 2017. "Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems," Applied Energy, Elsevier, vol. 189(C), pages 157-176.
    4. Christopher M. Weible & Daniel Nohrstedt & Paul Cairney & David P. Carter & Deserai A. Crow & Anna P. Durnová & Tanya Heikkila & Karin Ingold & Allan McConnell & Diane Stone, 2020. "COVID-19 and the policy sciences: initial reactions and perspectives," Policy Sciences, Springer;Society of Policy Sciences, vol. 53(2), pages 225-241, June.
    5. Madurai Elavarasan, Rajvikram & Shafiullah, GM & Raju, Kannadasan & Mudgal, Vijay & Arif, M.T. & Jamal, Taskin & Subramanian, Senthilkumar & Sriraja Balaguru, V.S. & Reddy, K.S. & Subramaniam, Umashan, 2020. "COVID-19: Impact analysis and recommendations for power sector operation," Applied Energy, Elsevier, vol. 279(C).
    6. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
    7. Bialek, Janusz, 2020. "What does the GB power outage on 9 August 2019 tell us about the current state of decarbonised power systems?," Energy Policy, Elsevier, vol. 146(C).
    8. Bialek, J., 2020. "What does the power outage on 9 August 2019 tell us about GB power system," Cambridge Working Papers in Economics 2018, Faculty of Economics, University of Cambridge.
    9. Yan Gao & Yan Cui, 2020. "Deep transfer learning for reducing health care disparities arising from biomedical data inequality," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    10. Janusz Bialek, 2020. "What does the power outage on 9 August 2019 tell us about GB power system," Working Papers EPRG2006, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    11. Qian, Fanyue & Gao, Weijun & Yang, Yongwen & Yu, Dan, 2020. "Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption," Energy, Elsevier, vol. 193(C).
    12. Zhang, Chi & Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2017. "On electricity consumption and economic growth in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 353-368.
    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. Lalitpat Aswanuwath & Warut Pannakkong & Jirachai Buddhakulsomsiri & Jessada Karnjana & Van-Nam Huynh, 2023. "An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand," Energies, MDPI, vol. 17(1), pages 1-31, December.
    2. Silvia Colabianchi & Margherita Bernabei & Francesco Costantino & Elpidio Romano & Andrea Falegnami, 2023. "MARLIN Method: Enhancing Warehouse Resilience in Response to Disruptions," Logistics, MDPI, vol. 7(4), pages 1-34, December.

    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. Qian, Fanyue & Gao, Weijun & Yang, Yongwen & Yu, Dan, 2020. "Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption," Energy, Elsevier, vol. 193(C).
    2. Siqiong Yao & Fang Dai & Peng Sun & Weituo Zhang & Biyun Qian & Hui Lu, 2024. "Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Stelios C. Dimoulias & Eleftherios O. Kontis & Grigoris K. Papagiannis, 2022. "Inertia Estimation of Synchronous Devices: Review of Available Techniques and Comparative Assessment of Conventional Measurement-Based Approaches," Energies, MDPI, vol. 15(20), pages 1-30, October.
    4. Harri Aaltonen & Seppo Sierla & Rakshith Subramanya & Valeriy Vyatkin, 2021. "A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage," Energies, MDPI, vol. 14(17), pages 1-20, September.
    5. Ziqian Zhang & Carina Lehmal & Philipp Hackl & Robert Schuerhuber, 2022. "Transient Stability Analysis and Post-Fault Restart Strategy for Current-Limited Grid-Forming Converter," Energies, MDPI, vol. 15(10), pages 1-26, May.
    6. Tomasz Cieślik & Piotr Narloch & Adam Szurlej & Krzysztof Kogut, 2022. "Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland," Energies, MDPI, vol. 15(4), pages 1-18, February.
    7. Bialek, Janusz, 2020. "What does the GB power outage on 9 August 2019 tell us about the current state of decarbonised power systems?," Energy Policy, Elsevier, vol. 146(C).
    8. Muhammad Shahid Mastoi & Hafiz Mudassir Munir & Shenxian Zhuang & Mannan Hassan & Muhammad Usman & Ahmad Alahmadi & Basem Alamri, 2022. "A Critical Analysis of the Impact of Pandemic on China’s Electricity Usage Patterns and the Global Development of Renewable Energy," IJERPH, MDPI, vol. 19(8), pages 1-30, April.
    9. Tomasz Jałowiec & Henryk Wojtaszek, 2021. "Analysis of the RES Potential in Accordance with the Energy Policy of the European Union," Energies, MDPI, vol. 14(19), pages 1-33, September.
    10. Zhongdong Yu & Wei Liu & Liming Chen & Serkan Eti & Hasan Dinçer & Serhat Yüksel, 2019. "The Effects of Electricity Production on Industrial Development and Sustainable Economic Growth: A VAR Analysis for BRICS Countries," Sustainability, MDPI, vol. 11(21), pages 1-13, October.
    11. Xu, Guangyue & Yang, Hualiu & Schwarz, Peter, 2022. "A strengthened relationship between electricity and economic growth in China: An empirical study with a structural equation model," Energy, Elsevier, vol. 241(C).
    12. Sharifah Nurain Syed Nasir & Norasikin Ahmad Ludin & Ahmad Afif Safwan Mohd Radzi & Mirratul Mukminah Junedi & Norhashimah Ramli & Anezah Marsan & Zul Fauzi Azlan Mohd & Muhamad Roszaini Roslan & Zulf, 2023. "Lockdown impact on energy consumption in university building," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 12051-12070, October.
    13. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    14. Witold Klaus, 2021. "The Porous Border Woven with Prejudices and Economic Interests. Polish Border Admission Practices in the Time of COVID-19," Social Sciences, MDPI, vol. 10(11), pages 1-13, November.
    15. Burleyson, Casey D. & Rahman, Aowabin & Rice, Jennie S. & Smith, Amanda D. & Voisin, Nathalie, 2021. "Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States," Applied Energy, Elsevier, vol. 304(C).
    16. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.
    17. Dong, Xiao-Ying & Hao, Yu, 2018. "Would income inequality affect electricity consumption? Evidence from China," Energy, Elsevier, vol. 142(C), pages 215-227.
    18. Romanus Osabohien & Oluwatoyin Matthew & Precious Ohalete & Evans Osabuohien, 2020. "Population–Poverty–Inequality Nexus and Social Protection in Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(2), pages 575-598, September.
    19. Kurz, Konstantin & Bock, Carolin & Knodt, Michèle & Stöckl, Anna, 2022. "A Friend in Need Is a Friend Indeed? Analysis of the Willingness to Share Self-Produced Electricity During a Long-lasting Power Outage," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 136773, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    20. Chengzhou Li & Ningling Wang & Hongyuan Zhang & Qingxin Liu & Youguo Chai & Xiaohu Shen & Zhiping Yang & Yongping Yang, 2019. "Environmental Impact Evaluation of Distributed Renewable Energy System Based on Life Cycle Assessment and Fuzzy Rough Sets," Energies, MDPI, vol. 12(21), pages 1-17, November.

    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:eee:appene:v:309:y:2022:i:c:s0306261921016834. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.