Assessment of deep recurrent neural network-based strategies for short-term building energy predictions

Abstract

Accurate and reliable building energy predictions can bring significant benefits for energy conservations. With the development in smart buildings, massive amounts of building operational data are being collected and available for analysis. It is desired to develop big data-driven methods to fully realize the potential of building operational data in energy predictions. This paper investigates the usefulness of advanced recurrent neural network-based strategies for building energy predictions. Each strategy presents unique characteristics at two levels. At the high level, three inference approaches are used for generating short-term predictions, including the recursive approach, the direct approach and the multi-input and multi-output (MIMO) approach. At the low level, the state-of-the-art techniques are utilized for recurrent model development, such as the use of one-dimensional convolutional operations, bidirectional operations, and different types of recurrent units. The performance of different strategies has been assessed from different perspectives based on real building operational data. The research results help to bridge the knowledge gap between building professionals and advanced big data analytics. The insights obtained can be used as guidelines and references for developing advanced deep recurrent models for short-term building energy predictions.