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Load prediction in short-term implementing the multivariate quantile regression

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  • Xing, Yazhou
  • Zhang, Su
  • Wen, Peng
  • Shao, Limin
  • Rouyendegh, Babak Daneshvar

Abstract

Probability-based interim demand prediction plays and important role in managing the grid and optimizing the transmitted power through lines. Improved prediction techniques able to offer precise forecasting are supposed to be compatible with their own implementational situations in interim operation and must be highly efficient and fast. A lot of prediction techniques based on data are excessively verbose and not very suitable. The mentioned challenge emerges when the numerous demands are supposed to be forecasted at the same time, for example assessing and optimizing the energy delivery network. Here, a novel hybrid prediction framework is suggested, which improves the probability-based prediction of each load in real-time. The improvement approach uses the multi-variable quantile regression that is implemented on each prediction in real-time when a new observational data is inputted to the system. The proposed procedure is assessed using the demand data released by the Independent System Operator-new England for eight areas, which is composed of six states of the U.S. The performance of the probability-based prediction are compared to that of three other benchmarks with respect to reliability and accuracy. The suggested approach shows better accuracy compared to the highest-ranked benchmark.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220301420
    DOI: 10.1016/j.energy.2020.117035
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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).
    3. 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).

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