IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v207y2020ics0360544220313724.html
   My bibliography  Save this article

A novel ensemble method for residential electricity demand forecasting based on a novel sample simulation strategy

Author

Listed:
  • Zhang, Guoqiang
  • Guo, Jifeng

Abstract

This paper presents a novel ensemble method of forecasting the residential electricity demand. Firstly, the time-series of the original input variables is filtered by unscented kalman filter (UKF), and then the incremental percentages of current and previous sample points are taken as new input features of the proposed method. Secondly, an improved coupled generative adversarial stacked auto-encoder (ICoGASA) consisting of three generative adversarial networks (GAN) is developed to generate more similar errors in weather forecast and lifestyles of different residents, with less noise. All of the three GANs are composed of two deep belief networks (DBNs), which serve as generator and discriminator, respectively. The three generators of GANs are used to simulate the samples with positive error, negative error and mixed error, respectively. Then the output of the three discriminators is integrated by memristor array (MA), and the integrated output of each ICoGASA are integrated by self-organizing map (SOM). Thirdly, the input weights of SOM are optimized by MA and a new weight updated strategy (WUS). Compared with other state-of-the-art ensemble methods, the scopes of the root mean square error (RMSE) are reduced by [8.295, 16.221] %, [15.507, 28.066] %, [20.494, 36.969] %, respectively.

Suggested Citation

  • Zhang, Guoqiang & Guo, Jifeng, 2020. "A novel ensemble method for residential electricity demand forecasting based on a novel sample simulation strategy," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313724
    DOI: 10.1016/j.energy.2020.118265
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220313724
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118265?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. Xing, Yazhou & Zhang, Su & Wen, Peng & Shao, Limin & Rouyendegh, Babak Daneshvar, 2020. "Load prediction in short-term implementing the multivariate quantile regression," Energy, Elsevier, vol. 196(C).
    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, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    2. Zheyu He & Rongheng Lin & Budan Wu & Xin Zhao & Hua Zou, 2023. "Pre-Attention Mechanism and Convolutional Neural Network Based Multivariate Load Prediction for Demand Response," Energies, MDPI, vol. 16(8), pages 1-13, April.
    3. Luo, Zheng & Lin, Xiaojie & Qiu, Tianyue & Li, Manjie & Zhong, Wei & Zhu, Lingkai & Liu, Shuangcui, 2024. "Investigation of hybrid adversarial-diffusion sample generation method of substations in district heating system," Energy, Elsevier, vol. 288(C).
    4. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).

    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. Zhang, Wenyu & Chen, Qian & Yan, Jianyong & Zhang, Shuai & Xu, Jiyuan, 2021. "A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting," Energy, Elsevier, vol. 236(C).
    2. Zheyu He & Rongheng Lin & Budan Wu & Xin Zhao & Hua Zou, 2023. "Pre-Attention Mechanism and Convolutional Neural Network Based Multivariate Load Prediction for Demand Response," Energies, MDPI, vol. 16(8), pages 1-13, April.

    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:energy:v:207:y:2020:i:c:s0360544220313724. 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.journals.elsevier.com/energy .

    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.