IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v177y2022ics0040162522000555.html
   My bibliography  Save this article

Comparison and application potential analysis of autoencoder-based electricity pattern mining algorithms for large-scale demand response

Author

Listed:
  • Li, Tong
  • Wang, Zhaohua
  • Zhao, Wenhui

Abstract

Studies of electricity consumption behavior patterns (ECBPs) are very important for demand-side management and emission reduction. Most of the existing ECBPs studies have limitations in data volume, algorithm performance and application potential in large-scale data scenario. Based on various autoencoders, this study proposes an ECBPs mining method with better performance and wider application potential. Two indices are constructed for model evaluations. The empirical research results based on the high-frequency power consumption data of residents show that the representation learning of ECBPs through autoencoders can not only effectively reduce the data dimension (reduced by 90%) for pattern mining but also achieve an effect no less than that of pattern mining from the original data, with the difference in pattern aggregation degree between them being only 0.003. Different autoencoders have characteristics in the representation mapping of the original data space. The results also show that the curves decoded by different autoencoders reflect different characteristics suitable for different scenarios. This study solves the dimension disaster problem of ECBPs mining in the context of large-scale data, and provides a better tool for more complex ECBPs based tasks such as multi-energy prosumers modeling and energy system optimization.

Suggested Citation

  • Li, Tong & Wang, Zhaohua & Zhao, Wenhui, 2022. "Comparison and application potential analysis of autoencoder-based electricity pattern mining algorithms for large-scale demand response," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:tefoso:v:177:y:2022:i:c:s0040162522000555
    DOI: 10.1016/j.techfore.2022.121523
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522000555
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2022.121523?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. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    2. Peng Liu & Peijun Zheng & Ziyu Chen, 2019. "Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 12(12), pages 1-15, June.
    3. Wan, Bingyue & Tian, Lixin & Zhu, Naiping & Gu, Liqin & Zhang, Guangyong, 2018. "A new endogenous growth model for green low-carbon behavior and its comprehensive effects," Applied Energy, Elsevier, vol. 230(C), pages 1332-1346.
    4. Yilmaz, S. & Chambers, J. & Patel, M.K., 2019. "Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management," Energy, Elsevier, vol. 180(C), pages 665-677.
    5. Xuan Liu & Qiancheng Wang & Hsi-Hsien Wei & Hung-Lin Chi & Yaotian Ma & Izzy Yi Jian, 2020. "Psychological and Demographic Factors Affecting Household Energy-Saving Intentions: A TPB-Based Study in Northwest China," Sustainability, MDPI, vol. 12(3), pages 1-20, January.
    6. Wang, Zhaohua & Zhao, Wenhui & Deng, Nana & Zhang, Bin & Wang, Bo, 2021. "Mixed data-driven decision-making in demand response management: An empirical evidence from dynamic time-warping based nonparametric-matching DID," Omega, Elsevier, vol. 100(C).
    7. Tan, Sieting & Yang, Jin & Yan, Jinyue & Lee, Chewtin & Hashim, Haslenda & Chen, Bin, 2017. "A holistic low carbon city indicator framework for sustainable development," Applied Energy, Elsevier, vol. 185(P2), pages 1919-1930.
    8. Nguyen, H.D. & Tran, K.P. & Thomassey, S. & Hamad, M., 2021. "Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management," International Journal of Information Management, Elsevier, vol. 57(C).
    9. Shang, Nan & Lin, You & Ding, Yi & Ye, Chengjin & Yan, Jinyue, 2019. "Nodal market power assessment of flexible demand resources," Applied Energy, Elsevier, vol. 235(C), pages 564-577.
    10. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    11. Torriti, Jacopo & Hassan, Mohamed G. & Leach, Matthew, 2010. "Demand response experience in Europe: Policies, programmes and implementation," Energy, Elsevier, vol. 35(4), pages 1575-1583.
    12. Carroll, James & Lyons, Seán & Denny, Eleanor, 2014. "Reducing household electricity demand through smart metering: The role of improved information about energy saving," Energy Economics, Elsevier, vol. 45(C), pages 234-243.
    Full references (including those not matched with items on IDEAS)

    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. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
    2. Do-Hyeon Ryu & Ryu-Hee Kim & Seung-Hyun Choi & Kwang-Jae Kim & Young Myoung Ko & Young-Jin Kim & Minseok Song & Dong Gu Choi, 2020. "Utilizing Electricity Consumption Data to Assess the Noise Discomfort Caused by Electrical Appliances between Neighbors: A Case Study of a Campus Apartment Building," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    3. Valdes, Javier & Masip Macia, Yunesky & Dorner, Wolfgang & Ramirez Camargo, Luis, 2021. "Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications," Energy, Elsevier, vol. 215(PA).
    4. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
    5. Yilmaz, S. & Weber, S. & Patel, M.K., 2019. "Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes," Energy Policy, Elsevier, vol. 133(C).
    6. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
    7. Troy Malatesta & Jessica K. Breadsell, 2022. "Identifying Home System of Practices for Energy Use with K-Means Clustering Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    8. Walker, Shalika & Bergkamp, Vince & Yang, Dujuan & van Goch, T.A.J. & Katic, Katarina & Zeiler, Wim, 2021. "Improving energy self-sufficiency of a renovated residential neighborhood with heat pumps by analyzing smart meter data," Energy, Elsevier, vol. 229(C).
    9. Elena Vechkinzova & Yelena Petrenko & Yana S. Matkovskaya & Gaukhar Koshebayeva, 2021. "The Dilemma of Long-Term Development of the Electric Power Industry in Kazakhstan," Energies, MDPI, vol. 14(9), pages 1-21, April.
    10. Xingwei Li & Jianguo Du & Hongyu Long, 2019. "Green Development Behavior and Performance of Industrial Enterprises Based on Grounded Theory Study: Evidence from China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    11. Schlör, Holger & Venghaus, Sandra & Hake, Jürgen-Friedrich, 2018. "The FEW-Nexus city index – Measuring urban resilience," Applied Energy, Elsevier, vol. 210(C), pages 382-392.
    12. Chen Zhang & Tao Yang, 2023. "Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training," Energies, MDPI, vol. 16(19), pages 1-18, October.
    13. Majdalani, Naim & Aelenei, Daniel & Lopes, Rui Amaral & Silva, Carlos Augusto Santo, 2020. "The potential of energy flexibility of space heating and cooling in Portugal," Utilities Policy, Elsevier, vol. 66(C).
    14. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    15. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Agent-based models and economic policy," Sciences Po publications info:hdl:2441/53r60a8s3ku, Sciences Po.
    16. Liu, Yingqi, 2017. "Demand response and energy efficiency in the capacity resource procurement: Case studies of forward capacity markets in ISO New England, PJM and Great Britain," Energy Policy, Elsevier, vol. 100(C), pages 271-282.
    17. Abdul Conteh & Mohammed Elsayed Lotfy & Kiptoo Mark Kipngetich & Tomonobu Senjyu & Paras Mandal & Shantanu Chakraborty, 2019. "An Economic Analysis of Demand Side Management Considering Interruptible Load and Renewable Energy Integration: A Case Study of Freetown Sierra Leone," Sustainability, MDPI, vol. 11(10), pages 1-19, May.
    18. Du, Xiaoyun & Meng, Conghui & Guo, Zhenhua & Yan, Hang, 2023. "An improved approach for measuring the efficiency of low carbon city practice in China," Energy, Elsevier, vol. 268(C).
    19. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    20. Lin, Boqiang & Zhu, Junpeng, 2019. "Impact of energy saving and emission reduction policy on urban sustainable development: Empirical evidence from China," Applied Energy, Elsevier, vol. 239(C), pages 12-22.

    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:tefoso:v:177:y:2022:i:c:s0040162522000555. 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.sciencedirect.com/science/journal/00401625 .

    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.