Accurate building energy consumption prediction is crucial to the rational planning of building energy systems. The energy consumption of buildings is influenced by various elements and is characterized by non-linearity and non-stationarity. To fully tap the time series characteristics of building energy consumption and heighten the model's prediction accuracy, this paper proposes a hybrid neural network prediction model combining attention mechanism, Bidirectional Gate Recurrent Unit (BiGRU), Convolutional Neural Networks (CNN), and the residual connection. The model uses BiGRU to train the e...
