基于A-DLSTM夹层网络结构的电能消耗预测方法

全球人口的快速增长和技术进步极大地提高了世界的总发电量,电能消耗预测对于电力系统调度和发电量管理发挥着重要的作用,为了提高电能消耗的预测精度,针对能耗数据的复杂时序特性,文中提出了一种将注意力机制(Attention)放置于双层长短期记忆人工神经网络(Double layer Long Short-Term Memory,DLSTM)中的新颖夹层结构,即A-DLSTM。该网络结构利用夹层中的注意力机制自适应地关注单个时间单元中不同的特征量,通过双层LSTM网络对序列中的时间信息进行抓取,以对序列数据进行预测。文中的实验数据为UCI机器学习数据集上某家庭近5年的用电量,采用网格搜索法进行调参,实验对比了A-DLSTM与现有的模型在能耗数据上的预测性能,文中的网络结构在均方误差、均方根误差、平均绝对误差、平均绝对百分比误差上均达到了最优,且通过热力图对注意力层进行了分析,确定了对用电量预测影响最大的因素。

Predicting Electric Energy Consumption Using Sandwich Structure of Attention in Double-LSTM

The rapid growth of the global population and technological progress has significantly increased the world’s total power generation.Electric energy consumption forecasts play an essential role in power system dispatch and power generation management.Aim at the complex characteristics of time series of energy consumption data,and to improve the prediction accuracy of power consumption,a novel sandwich structure is proposed,in which an Attention mechanism is placed in the double layer long short-term memory artificial neural network,namely A-DLSTM.This network structure uses the attention mechanism in the mezzanine to adaptively focus on different features in each single time unit and uses the two-layer LSTM network to capture the time information in the sequence to predict the sequence data.The experimental data comes from the UCI machine learning data set,and it is the electricity consumption of a family in the past five years.The parameters of the experiment are adjusting by the grid search method.The experiment compares the prediction performance of A-DLSTM and the existing model on energy consumption data.The network of this article reaches the state-of-the-art in terms of mean square error,root mean square error,average absolute error,and average absolute percentage error.By analyzing the heat map’s attention layer,the factor that has the most significant impact on electricity consumption forecasting is determined.