Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption

Accurate energy consumption prediction models can bring tremendous benefits to building energy efficiency, where the use of data-driven models allows models to be trained based on historical data and to obtain better prediction performance. However, a severe problem has been overlooked where the dynamic spatiotemporal dependence among buildings is often not considered. To settle this issue, this study proposes an asymmetric encoder-decoder learning framework where the spatial relationships and time-series features between multiple buildings are extracted by a convolutional neural network and a gated recurrent neural network to form new input data in the encoder. The decoder then makes predictions based on the input data with an attention mechanism. The model is trained and tested with energy consumption data from 10 different buildings at a university in China. The results show that our proposed model maintains high accuracy using small datasets while predicting different types of buildings, achieving an average R2 of 0.964. Additionally, the accuracy loss in multi-step forecasting is considerably ameliorated by maintaining an average R2 of 0.915 predicting 3 steps ahead of time. Benefiting from the asymmetric encoder-decoder structure, the proposed algorithm resolves the problem of accuracy loss using deep learning in a small dataset and multi-step time series forecasting. Further assisted by the anomaly detection function, the algorithm serves as a reliable guide for the user to effectively manage and control energy consumption in buildings.