A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism

This paper proposes a short-term electric load forecasting method combining convolutional neural networks and gated recurrent units with an attention mechanism. By integrating CNNs and GRUs, the method can fully leverage the strengths of CNNs in feature extraction and the advantages of GRUs in seque...

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Vydané v:Energies (Basel) Ročník 18; číslo 1; s. 106
Hlavní autori: Hua, Qingbo, Fan, Zengliang, Mu, Wei, Cui, Jiqiang, Xing, Rongxin, Liu, Huabo, Gao, Junwei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.01.2025
Predmet:
Accuracy
Algorithms
attention mechanism
convolutional neural network
Deep learning
Energy consumption
Environmental protection
Forecasting
Machine learning
Neural networks
power load forecasting
Power supply
Prediction theory
Support vector machines
Time series
China
ISSN:1996-1073, 1996-1073
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Shrnutí:This paper proposes a short-term electric load forecasting method combining convolutional neural networks and gated recurrent units with an attention mechanism. By integrating CNNs and GRUs, the method can fully leverage the strengths of CNNs in feature extraction and the advantages of GRUs in sequence modeling, enabling the model to comprehend signal data more comprehensively and effectively extract features from sequential data. The introduction of the attention mechanism allows the traditional model to dynamically focus on important parts of the input data while ignoring the unimportant parts. This capability enables the model to utilize input information more efficiently, thereby enhancing model performance. This paper applies the proposed model to a dataset comprising regional electric load and meteorological data for experimentation. The results show that compared with other common models, the proposed model effectively reduces the mean absolute error and root mean square error (121.51 and 263.43, respectively) and accurately predicts the short-term change in regional power load.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en18010106