A hybrid prediction interval model for short-term electric load forecast using Holt-Winters and Gate Recurrent Unit

He, Xin; Zhao, Wenlu; Gao, Zhijun; Zhang, Qiushi; Wang, Wen

doi:10.1016/j.segan.2024.101343

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2024

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Cited by 3 publications

References 66 publications

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Study on Optimization Method for CNC Machining Plastic-Shaped Appliances Based on ICOA Algorithm

Chen,

Zhou,

Zhao

et al. 2024

Int. J. Precis. Eng. Manuf.

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Study on Optimization Method for CNC Machining Plastic-Shaped Appliances Based on ICOA Algorithm

Chen,

Zhou,

Zhao

et al. 2024

Int. J. Precis. Eng. Manuf.

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Intelligent Short-Term Hybrid Forecasting Model Applied on a Community-Based Home Energy Management System

Osório,

Teixeira-Lopes,

Javadi

et al. 2024

2024 International Conference on Smart Energy Systems and Technologies (SEST)

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Short-Term Power Load Forecasting Based on Secondary Cleaning and CNN-BILSTM-Attention

Wang,

Li,

2024

Energies

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Accurate power load forecasting can provide crucial insights for power system scheduling and energy planning. In this paper, to address the problem of low accuracy of power load prediction, we propose a method that combines secondary data cleaning and adaptive variational mode decomposition (VMD), convolutional neural networks (CNN), bi-directional long short-term memory (BILSTM), and adding attention mechanism (AM). The Inner Mongolia electricity load data were first cleaned use the K-means algorithm, and then further refined with the density-based spatial clustering of applications with the noise (DBSCAN) algorithm. Subsequently, the parameters of the VMD algorithm were optimized using a multi-strategy Cubic-T dung beetle optimization algorithm (CTDBO), after which the VMD algorithm was employed to decompose the twice-cleaned load sequences into a number of intrinsic mode functions (IMFs) with different frequencies. These IMFs were then used as inputs to the CNN-BILSTM-Attention model. In this model, a CNN is used for feature extraction, BILSTM for extracting information from the load sequence, and AM for assigning different weights to different features to optimize the prediction results. It is proved experimentally that the model proposed in this paper achieves the highest prediction accuracy and robustness compared to other models and exhibits high stability across different time periods.

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A hybrid prediction interval model for short-term electric load forecast using Holt-Winters and Gate Recurrent Unit

Cited by 3 publications

References 66 publications

Study on Optimization Method for CNC Machining Plastic-Shaped Appliances Based on ICOA Algorithm

Study on Optimization Method for CNC Machining Plastic-Shaped Appliances Based on ICOA Algorithm

Intelligent Short-Term Hybrid Forecasting Model Applied on a Community-Based Home Energy Management System

Short-Term Power Load Forecasting Based on Secondary Cleaning and CNN-BILSTM-Attention

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