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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1443814
This article is part of the Research Topic Optimal Scheduling of Demand Response Resources In Energy Markets For High Trading Revenue and Low Carbon Emissions View all 25 articles

Research on urban power load forecasting based on improved LSTM

Provisionally accepted
Zhou Zhenglei Zhou Zhenglei 1Jun Chen Jun Chen 1Zhou Yang Zhou Yang 1*Wenguang Wu Wenguang Wu 2Hong Ding Hong Ding 2
  • 1 Guangxi Power Grid Corporation, Nanning, China
  • 2 Nari Group Corporation State Grid Electric Power Research Institute, Nanjing, Liaoning Province, China

The final, formatted version of the article will be published soon.

    In this paper, the maximal information coefficient method-variational mode decompositionbidirectional long short term memory network-adaptive boosting (MIC-VMD-Bi-LSTM-Adaboost) algorithm is used to forecast the power load. Firstly, MIC is used to determine the correlation degree of meteorological parameters influencing power load. Features having a high correlation degree are then chosen to be input vectors. Secondly, the input characteristics are decomposed using VMD, and five distinct IMF components are retrieved in order to remove unnecessary information. Lastly, different assessment indices are computed and the power load is predicted using the Bi-LSTM-Adaboost method. In order to determine the benefit of the approach used in this work, the outcomes of LSTM, Bi-LSTM, and LSTM-Adaboost are compared concurrently.

    Keywords: Power load forecasting, MIC, VMD, LSTM, Adaboost

    Received: 04 Jun 2024; Accepted: 17 Oct 2024.

    Copyright: © 2024 Zhenglei, Chen, Yang, Wu and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Zhou Yang, Guangxi Power Grid Corporation, Nanning, China

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