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A decomposition-based approximate entropy cooperation long short-term memory ensemble model for short-term load forecasting.
- Source :
-
Electrical Engineering . Jun2022, Vol. 104 Issue 3, p1515-1525. 11p. - Publication Year :
- 2022
-
Abstract
- Short-term load forecasting with high accuracy is essential to power systems. Because power loads involve high volatility and uncertainty, it is challenging to accurately perform short-term load forecasting (STLF). To solve this problem, this paper proposes a decomposition-based approximate entropy cooperation long short-term memory (DB-AEC-LSTM) model for STLF. In DB-AEC-LSTM, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is first introduced to generate the multiple electric load time series into many cooperation sub-series and decrease the reconstruction errors. Then, an Approximate Entropy Cooperation ensemble Long Short-term Memory Model is developed by using approximate entropy (ApEn) to construct an effective cooperative relationship between different time sub-series groups, greatly improving the predictive accuracy. By rationally combined the effective technologies ApEn, CEEMDAN, and AEC-LSTM, the proposed DB-AEC-LSTM can obtain competitive predictive performance in STLF. Several short-term load forecasting datasets are performed to check the predictive performance of DB-AEC-LSTM. Experimental results show that DB-AEC-LSTM has better predictive accuracy and satisfactory robustness compared with state-of-the-art and conventional predictive models. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09487921
- Volume :
- 104
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Electrical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 156889399
- Full Text :
- https://doi.org/10.1007/s00202-021-01389-0