A reinforcing transfer learning approach to predict buildings energy performance

Purpose: The purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the dynamic nature of building systems. Design/methodology/approach: This paper proposes a reinforcing machine learning (ML) approach based on transfer learning (TL) to address these challenges. The proposed approach dynamically incorporates the data captured by the building management systems into the model to improve its accuracy. Findings: It was shown that the proposed approach could improve the accuracy of the energy performance prediction compared to the conventional TL (non-reinforcing) approach by 19 percentage points in mean absolute percentage error. Research limitations/implications: The case study results confirm the practicality of the proposed approach and show that it outperforms the standard ML approach (with no transferred knowledge) when little data is available. Originality/value: This approach contributes to the body of knowledge by addressing the limited data availability in the building sector using TL; and accounting for the dynamics of buildings’ energy performance by the reinforcing architecture. The proposed approach is implemented in a case study project based in London, UK.