报告详情
Short-term Load Forecasting Method Based on PSO-VMD-BiLSTM-BiGRU Model
编号：141
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更新：2024-10-23 10:02:35 浏览：251次
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摘要
With the advancement of smart grid technologies and the emergence of novel energy systems, loads characterized by diversity and flexibility have become a critical component of these systems. Research on their prediction models is essential for the operation, maintenance, and planning of novel energy systems. This paper presents a short-term joint forecasting method for multi-variable loads based on PSO-VMD-LSTM-GRU, taking into account the coupling characteristics of multi-variable loads and the correlations with other factors. First, various correlation analysis methods are employed to study the coupling characteristics of multi-variable loads and the correlations of influencing factors, and predictive features are selected accordingly. Second, variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is utilized to decompose the multi-variable loads, thereby improving load utilization. Finally, LSTM and GRU models are employed to forecast the low-frequency and high-frequency components, respectively, and the predicted results are combined to obtain the final forecast. The effectiveness of the proposed method is validated through experiments using data from the ASHRAE-Great Energy Predictor III competition project, comparing it with conventional load forecasting methods.
关键词
Electric load forecasting; Correlation analysis; Multi-variable loads; VMD; LSTM; GRU
报告人
YinJiaojiao
Miss Harbin Institute of Technology稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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