Based on the analysis of factors such as seasonal and temperature changes,social safety events,and line configuration affecting the 10kV sub-district low voltage in Yunnan,China,this study proposes a mid-to-long term prediction method employing a CNN-LSTM-GNN hybrid model.Recognizing the impact of seasonal variations,tourism fluctuations due to pandemics,and the topology of power distribution networks,the research integrates Convolutional Neural Networks (CNN) for extracting temporal and seasonal load features,Long Short-Term Memory (LSTM) networks for learning and forgetting irrelevant features over time,and Graph Neural Networks (GNN) for understanding the complex relational data among different network nodes.Through comprehensive data preparation and model training using historical load,temperature,transmission distance,and holiday information as well as social event impacts,the proposed model demonstrates remarkable voltage prediction accuracy with nearly 100％ within a 1V error margin in the simulation,significantly outperforming traditional methods in terms of feature requirement,computational complexity,and accuracy.This approach not only offers a robust prediction model for managing low voltage issues across sub-districts but also proves efficient in extrapolating from power data to assess voltage situations across the entire network topology without needing extensive operational parameters.
关键词
电力负荷预测/低电压预测/深度学习/CNN-LSTM-GNN混合模型
Key words
CNN-LSTM-GNN hybrid model/low voltage prediction/deep learning/power load forecasting
万方数据