TCN-Attention-BIGRU: Building energy modelling based on attention mechanisms and temporal convolutional networks

Abstract

<abstract> <p>Accurate and effective building energy consumption prediction is an important basis for carrying out energy-saving evaluation and the main basis for building energy-saving optimization design. However, due to the influence of environmental and human factors, energy consumption prediction is often inaccurate. Therefore, this paper presents a building energy consumption prediction model based on an attention mechanism, time convolutional neural (TCN) network fusion, and a bidirectional gated cycle unit (BIGRU). First, t-distributed stochastic neighbor embedding (T-SNE) was used to preprocess the data and extract the key features, and then a BIGRU was employed to acquire past and future data while capturing immediate connections. Then, to catch the long-term dependence, the dataset was partitioned into the TCN network, and the extended sequence was transformed into several short sequences. Consequently, the gradient explosion or vanishing problem is mitigated when the BIGRU handles lengthy sequences while reducing the spatial complexity. Second, the self-attention mechanism was introduced to enhance the model's capability to address data periodicity. The proposed model is superior to the other four models in accuracy, with an mean absolute error of 0.023, an mean-square error of 0.029, and an coefficient of determination of 0.979. Experimental results indicate that T-SNE can significantly improve the model performance, and the accuracy of predictions can be improved by the attention mechanism and the TCN network.</p> </abstract>