摘要
为了解决区域级短期电力负荷预测时输入特征过多和负荷时序性较强的问题,提出一种基于集群辨识和卷积神经网络(convolutional neural networks,CNN)-双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)-时序模式注意力机制(temporal pattern attention,TPA)的预测方法。首先,将用电模式和天气作为影响因素,基于二阶聚类算法对区域内的负荷节点进行集群辨识,再从每个集群中挑选代表特征作为深度学习模型的输入,这样既能减少输入特征维度,降低计算复杂度,又能综合考虑预测区域的整体特征,提升预测精度。然后,针对区域电力负荷时序性的特点,用CNN-BiLSTM-TPA模型完成训练和预测,该模型能提取输入数据的双向信息生成隐状态矩阵,并对隐状态矩阵的重要特征加权,从多时间步上捕获双向时序信息用于预测。最后,在美国加利福尼亚州实例上分析验证了所提方法的有效性。
In this paper,a predictive method based on cluster identification and convolutional neural network-bi-directional long short-term memory-temporal pattern attention(CNN-BiLSTM-TPA)is proposed to solve the problem of excessive input features and strong load periodicity in regional short-term power load forecasting.Firstly,load nodes within the region are identified as clusters based on second-order clustering algorithm with consideration of power consumption mode and weather as the influence factors.And then,the representative features are selected from each cluster as inputs of the deep learning model,which can not only reduce the input feature dimension and decrease the computational complexity,but also comprehensively consider the overall characteristics of the prediction region to improve the prediction accuracy.Thereafter,aiming at the strong load periodicity of regional power load,the CNN-BiLSTM-TPA model is trained and applied for prediction,extracting the bi-directional information from the input data to generate the hidden state matrix and weighing the important features of the hidden state matrix,while capturing the bi-directional time series information on multiple time steps for prediction.Finally,the effectiveness of proposed method is verified using the actual load data in California,USA.
作者
陈晓梅
肖徐东
CHEN Xiaomei;XIAO Xudong(School of Electrical and Electronic Engineering,North China Electric Power University,Changping District,Beijing 102206,China)
出处
《现代电力》
北大核心
2024年第1期106-115,共10页
Modern Electric Power
关键词
短期电力负荷预测
双向长短期记忆网络
时序模式注意力机制
集群辨识
卷积神经网络
short-term power load forecasting
bi-directional long short-term memory network
temporal pattern attention
cluster identification
convolutional neural network
