Data Augmentation Using Multivariate Time Series Decomposition for Predicting Daily Energy Consumption of New Buildings

Predicting building energy consumption is an essential part of demand-side management since it enables cost-effective building operation under limited resources. Recent prediction models have adopted deep learning networks due to their high capabilities in extracting occupants’ energy use patterns from historical data. Additionally, augmenting historical data by decomposing existing input times-series into several temporal components can enhance prediction performance, particularly for new buildings. However, it still remains unclear how newly created predictors, through the decomposition of existing time-series, affect the performance of building energy use prediction. Therefore, to address this gap, this study proposed a deep learning-based energy use prediction framework that employs the Unobserved Component Model to create new input time-series based on existing ones. Then, the performance of the proposed prediction framework was evaluated using two years of historical data collected from a case building. The main findings are threefold. First, deep learning networks achieved a higher prediction performance during training than during testing. Second, testing performance was generally better when using the augmented dataset than the raw dataset. Third, the proposed data augmentation method contributes to a 1.26% decrease in MAPE and a 3.42% decrease in CvRMSE. This suggests that the proposed prediction framework can be applied to simulations of buildings with limited time series dataset to more accurately predict energy consumption at the building level.