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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1501963

An Optimized Method for Short-Term Load Forecasting Based on Feature Fusion and ConvLSTM-3D Neural Network

Provisionally accepted
Xiaofeng YANG Xiaofeng YANG Shousheng ZHAO Shousheng ZHAO Kangyi Li Kangyi Li *Wenjin CHEN Wenjin CHEN Si ZHANG Si ZHANG Jingwei CHEN Jingwei CHEN
  • State Grid Zhejiang Electric Power Co., Ltd Shaoxing Power Supply Company, Shaoxing, China, Shaoxing, China

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

    As renewable energy continues to penetrate modern power systems, accurate short-term load forecasting is crucial for optimizing power generation resource allocation and reducing operational costs. Traditional forecasting methods often overlook key factors such as holiday load variations and differences in user electricity consumption behavior, resulting in reduced accuracy. To address this, we propose an optimized short-term load forecasting method based on time and weather-fused features using a ConvLSTM-3D neural network. The Prophet algorithm is first employed to decompose historical electricity load data, extracting feature components related to time variables. Simultaneously, the SHAP algorithm filters weather variables to identify highly correlated weather features. A time attention mechanism is then applied to fuse these features based on their correlation weights, enhancing their impact within the time series. Finally, the ConvLSTM-3D model is trained on the fused features to generate short-term load forecasts. A case study using real-world data validates the proposed method, demonstrating significant improvements in forecasting accuracy.

    Keywords: Short-term load forecasting, Fused features, prophet algorithm, SHAP Algorithm, ConvLSTM-3D model

    Received: 26 Sep 2024; Accepted: 16 Dec 2024.

    Copyright: © 2024 YANG, ZHAO, Li, CHEN, ZHANG and CHEN. 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: Kangyi Li, State Grid Zhejiang Electric Power Co., Ltd Shaoxing Power Supply Company, Shaoxing, China, Shaoxing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.