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A power load forecasting model based on a combined neural network

Authors :
Jie Li
Chenguang Qiu
Yulin Zhao
Yuyang Wang
Source :
AIP Advances, Vol 14, Iss 4, Pp 045231-045231-11 (2024)
Publication Year :
2024
Publisher :
AIP Publishing LLC, 2024.

Abstract

The supply of electric power is vital for the daily lives of people, industrial production, and business services. At present, although enough electric power can be supplied to meet the power demand, there are still some challenges, especially in terms of long-distance power transmissions and long-term power storage. Consequently, if the power production capacity exceeds the immediate consumption requirements, i.e., the produced electric power cannot be consumed in a short period, and much electric power could be wasted. Evidently, to minimize the wastage of electric power, it is necessary to properly plan power production by accurately forecasting the future power load. Therefore, a preferable power load forecasting algorithm is crucial for the planning of power production. This paper proposes a novel deep learning model for the purpose of power load forecasting, termed the SSA-CNN-LSTM-ATT model, which combines the CNN-LSTM model with SSA optimization and attention mechanisms. In this model, the CNN module extracts the features from the sequential data, and then the features are passed to the LSTM module for modeling and capturing the long-term dependencies hidden in the sequences. Subsequently, an attention layer is employed to measure the importance of different features. Finally, the output is obtained through a fully connected layer, yielding the forecasting results of the power load. Extensive experiments have been conducted on a real-world dataset, and the metric R2 can reach 0.998, indicating that our proposed model can accurately forecast the power load.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
21583226
Volume :
14
Issue :
4
Database :
Directory of Open Access Journals
Journal :
AIP Advances
Publication Type :
Academic Journal
Accession number :
edsdoj.fd98a55b8ad24325814a58c66ae01612
Document Type :
article
Full Text :
https://doi.org/10.1063/5.0185448