Effective prediction of electric energy consumption is important for improving the accuracy of electric load time series measurement and formulating reasonable measures for electric energy consumption management.To address the problem that traditional prediction models cannot adequately capture the evolutionary patterns of time vari-ables in electric energy load prediction,this paper proposes a hybrid multi-hidden layer CNN-LSTM electric energy data time series based on the trend of electric energy data and fusing numerical information to propose a hybrid convo-lutional neural network and a long-term short-term memory recurrent neural network combined with a hybrid multi-hidden layer CNN-LSTM power energy prediction model.First,by setting the minimum objective function as the opti-mization objective,the Adam optimization algorithm updates the weights of the neural network and adaptively tunes the network layers and batch sizes to determine the optimal number of layers and batch sizes.Next,a hybrid multi-hidden layer model is constructed and the combination of hidden layers is optimized and discussed to determine the parame-ters of the optimal time dimension,and feature learning of the time dimension is performed to predict the power con-sumption of the next time series.Then the data set of a company's electricity load data is validated and compared with the prediction results of LSTM,CNN,RNN and other models.The results show that the prediction accuracy of this hy-brid multi-hidden layer model can reach 98.94% and the mean absolute error(MAE)reaches 0.0066,both of which are better than other basic models,proving that this combined prediction model has better performance in terms of power load prediction accuracy.Based on the above theory,an energy consumption intelligent monitoring system is developed to realize real-time equipment status monitoring and energy consumption intelligent prediction function,which provides reference and guidance to solve the problems of inaccurate energy demand and energy inventory waste in traditional manufacturing industry.
关键词
电力负荷预测/卷积神经网络/长短期记忆神经网络/混合多隐层组合模型
Key words
Electric load forecasting/Convolutional neural network/Long and short-term memory neural network/Hybrid multi-hidden layer combination model
维普
万方数据