| Issue |
E3S Web Conf.
Volume 522, 2024
2023 9th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2023)
|
|
|---|---|---|
| Article Number | 01017 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/e3sconf/202452201017 | |
| Published online | 07 May 2024 | |
Short-term power load forecasting using informer encoder and bi-directional LSTM
Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, Hubei, 443002, China
* Corresponding author: 1923295312@qq.com
An innovative model called InE-BiLSTM is proposed here, which combines the Informer Encoder with a bidirectional LSTM (Bi-LSTM) network. The goal is to enhance the precision and efficacy of short-term electricity load forecasting. By integrating the long-term dependency capturing capability of the informer encoder with the advantages of Bi-LSTM in handling dynamic features in time series data, the InE-BiLSTM model effectively addresses complex patterns and fluctuations in electricity load data. The study begins by analyzing the current state of short-term electricity load forecasting, followed by a detailed introduction to the structure and principles of the InE-BiLSTM model. Results of the experiment demonstrate that, compared to the Informer, traditional Bi-LSTM, and Transformer models, the InE-BiLSTM model consistently outperforms them across various evaluation metrics.
Key words: Short-term power load forecasting / InE-BiLSTM model / Informer encoder / Bi-LSTM
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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