关键词: 注意力机制, 卷积神经网络（CNN）, 长短期记忆网络（LSTM）, 时间序列, 负荷预测

Abstract: Time series have temporal property, and the characteristics of its short sequences are different in importance. Aiming at the characteristics of time series, a neural network prediction model based on Convolution Neural Network（CNN） and Long Short-Term Memory（LSTM） is proposed, which combines coarse and fine grain features to achieve accurate time series prediction. The model consists of two parts. CNN based on attention mechanism adds attention branch to standard CNN network to extract important fine-grained features. The back end is LSTM, which extracts the coarse-grained features of the hidden time series from fine-grained features. Experiments on real cogeneration heat load dataset demonstrate that the model is better than the autoregressive integrated moving average, support vector regression, CNN and LSTM models. Compared with the pre-determined method currently used by enterprises, the Mean Absolute Scaled Error（MASE） and Root Mean Square Error（RMSE） have been increased by 89.64% and 61.73% respectively.

Key words: attention mechanism, Convolution Neural Network（CNN）, Long Short-Term Memory Network（LSTM）, time series, load forecasting