Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention

Abstract

Building energy consumption prediction has always played a significant role in assessing building energy efficiency, building commissioning, and detecting and diagnosing building system faults. With the progress of society and economic development, building energy consumption is growing rapidly. Therefore, accurate and effective building energy consumption prediction is the basis of energy conservation. Although there are currently a large number of energy consumption research methods, each method has different applicability and advantages and disadvantages. This study proposes a Time Convolution Network model based on an attention mechanism, which combines the ability of the Time Convolution Network model to capture ultra-long time series information with the ability of the BiGRU model to integrate contextual information, improve model parallelism, and reduce the risk of overfitting. In order to tune the hyperparameters in the structure of this prediction model, such as the learning rate, the size of the convolutional kernel, and the number of recurrent units, this study chooses to use the Golden Jackal Optimization Algorithm for optimization. The study shows that this optimized model has better accuracy than models such as LSTM, SVM, and CNN.

Keywords

• energy consumption prediction
• temporal convolutional network (TCN)
• BiGRU
• attention
• golden jackal optimization