为确保电力系统的安全与稳定,需对未来一天内各时段的电力需求进行精准预测。然而,随着可再生能源的增加,电荷预测变得更加复杂和不可预测。因此,本文介绍了一个基于GCformer构建的新模型GGSAGCformer,通过图卷积神经网络和门控循环单...为确保电力系统的安全与稳定,需对未来一天内各时段的电力需求进行精准预测。然而,随着可再生能源的增加,电荷预测变得更加复杂和不可预测。因此,本文介绍了一个基于GCformer构建的新模型GGSAGCformer,通过图卷积神经网络和门控循环单元提取数据中的空间和时序特征,再引入多头注意力机制(Multi-head Attention Mechanism Layer)。在此层中加入了地理相似空间自注意力模块(Geographically Similar Spatial Self-Attention, GSSA)和气候自注意力模块(Climate Self-Attention, CSA),旨在深入探索数据中的潜在关联,输出层使用GCformer来处理预测结果,以提升预测的准确性。实验结果显示,本文模型在输出步长为192的情况下,与传统模型GCformer、Informer和Reformer相比,MSE分别降低了15.3%、25%和29.3%。In order to ensure the safety and stability of the power system, it is necessary to accurately predict the power demand at each time of the day in the future. However, with the increase in renewable energy, charge prediction has become more complex and unpredictable. Therefore, this paper introduces a new model based on GCformer, GGSAGCformer, which extracts spatial and temporal features from the data through graph convolutional neural network and gated recurrent unit, and then introduces the multi-head attention mechanism layer. The Geographically Similar Spatial Self-Attention (GSSA) and Climate Self-Attention (CSA) modules are added to this layer to deeply explore the potential associations in the data, and the output layer uses GCformer to process the prediction results to improve the accuracy of the prediction. The experimental results show that the MSE of the proposed model is reduced by 15.3%, 25% and 29.3% compared with the traditional models GCformer, Informer and Reformer when the output step size is 192, respectively.展开更多
文摘为确保电力系统的安全与稳定,需对未来一天内各时段的电力需求进行精准预测。然而,随着可再生能源的增加,电荷预测变得更加复杂和不可预测。因此,本文介绍了一个基于GCformer构建的新模型GGSAGCformer,通过图卷积神经网络和门控循环单元提取数据中的空间和时序特征,再引入多头注意力机制(Multi-head Attention Mechanism Layer)。在此层中加入了地理相似空间自注意力模块(Geographically Similar Spatial Self-Attention, GSSA)和气候自注意力模块(Climate Self-Attention, CSA),旨在深入探索数据中的潜在关联,输出层使用GCformer来处理预测结果,以提升预测的准确性。实验结果显示,本文模型在输出步长为192的情况下,与传统模型GCformer、Informer和Reformer相比,MSE分别降低了15.3%、25%和29.3%。In order to ensure the safety and stability of the power system, it is necessary to accurately predict the power demand at each time of the day in the future. However, with the increase in renewable energy, charge prediction has become more complex and unpredictable. Therefore, this paper introduces a new model based on GCformer, GGSAGCformer, which extracts spatial and temporal features from the data through graph convolutional neural network and gated recurrent unit, and then introduces the multi-head attention mechanism layer. The Geographically Similar Spatial Self-Attention (GSSA) and Climate Self-Attention (CSA) modules are added to this layer to deeply explore the potential associations in the data, and the output layer uses GCformer to process the prediction results to improve the accuracy of the prediction. The experimental results show that the MSE of the proposed model is reduced by 15.3%, 25% and 29.3% compared with the traditional models GCformer, Informer and Reformer when the output step size is 192, respectively.
