Abstract
Accurate short-term residential load forecasting is essential for the efficient operation of power systems. However, existing forecasting methods face several challenges, including the inability to effectively handle the rapid accumulation of residential load data, the complexity of capturing patterns and dependencies, and the limited consideration of relative factors influencing load behaviours. This paper proposes a novel approach for short-term residential load forecasting that integrates Long Short-Term Memory (LSTM) and attention mechanism (AM) with trigonometric encoding to capture periodic and seasonal variations. Additionally, Season Trending Loess (STL) decomposition method is applied to identify seasonal trends in the data. Key input features include historical load data, price data, and solar and wind generation data. The dual-path mechanism optimizes system performance using mean squared error (MSE) for the proposed ensembled method. This approach provides highly accurate short-term load forecasts, enhancing power system management. Finally, the proposed model achieves significant improvements, with gains of 28.71%, 15.57%, and 17.99% in MSE, RMSE, and MAE, respectively, compared to existing methods.
Original language | English |
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Title of host publication | 2024 4th International Symposium on Electrical, Electronics and Information Engineering (ISEEIE) |
Place of Publication | Piscataway, US |
Publisher | IEEE |
Pages | 450-456 |
Number of pages | 7 |
ISBN (Electronic) | 9798350355772 |
ISBN (Print) | 9798350355789 |
DOIs | |
Publication status | Published - 28 Aug 2024 |
Event | 4th International Symposium on Electrical, Electronics and Information Engineering - University of Leicester, Leicester, United Kingdom Duration: 28 Aug 2024 → 30 Aug 2024 https://conferences.ieee.org/conferences_events/conferences/conferencedetails/62461 |
Conference
Conference | 4th International Symposium on Electrical, Electronics and Information Engineering |
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Abbreviated title | ISEEIE 2024 |
Country/Territory | United Kingdom |
City | Leicester |
Period | 28/08/24 → 30/08/24 |
Internet address |
Keywords
- short-term residential load forecasting
- trigonometric encoding
- STL Decomposition
- LSTM
- AM