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| Low-voltage distribution area load forecasting based on temporal convolutional network and crisscross optimization algorithm |
| DOI：10.19783/j.cnki.pspc.241617 |
| Key Words:low-voltage distribution area load forecasting crisscross optimization algorithm temporal convolutional network convolutional block attention module |
| Author Name | Affiliation | | DING Weifeng1 | 1. CSG EHV Electric Power Research Institute, Guangzhou 510620, China
2. School of Automation,
Guangdong University of Technology, Guangzhou 510006, China | | ZHOU Zhenzhen1 | 1. CSG EHV Electric Power Research Institute, Guangzhou 510620, China
2. School of Automation,
Guangdong University of Technology, Guangzhou 510006, China | | XIE Zhenhua2 | 1. CSG EHV Electric Power Research Institute, Guangzhou 510620, China
2. School of Automation,
Guangdong University of Technology, Guangzhou 510006, China | | XIAO Yaohui1 | 1. CSG EHV Electric Power Research Institute, Guangzhou 510620, China
2. School of Automation,
Guangdong University of Technology, Guangzhou 510006, China | | HUANG Heyan1 | 1. CSG EHV Electric Power Research Institute, Guangzhou 510620, China
2. School of Automation,
Guangdong University of Technology, Guangzhou 510006, China | | HE Sen1 | 1. CSG EHV Electric Power Research Institute, Guangzhou 510620, China
2. School of Automation,
Guangdong University of Technology, Guangzhou 510006, China |
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| Abstract:Accurate power load forecasting is crucial for the operation and maintenance of low-voltage distribution areas. To improve the accuracy of power load forecasting, this paper proposes a low-voltage load forecasting model that integrates a crisscross optimization algorithm (CSO) with a convolutional block attention module (CBAM) and a temporal convolutional network (TCN). First, a forecasting model is established based on TCN to extract the implicit temporal patterns of the input sequence of power loads. Second, a CBAM module is introduced at the model input side to apply channel-wise and spatial-wise weighting, thereby enhancing the model’s sensitivity to key features. Finally, to address issues such as local optima and limited generalization, the CSO algorithm is proposed to perform secondary optimization on the fully connected layer of the CBAM-TCN model. Using real power load datasets from two typical low-voltage substations in Guangdong province for simulation and modelling, the results show that the proposed hybrid forecasting method outperforms other comparative models and effectively validates its superiority. |
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