A hybrid-model forecasting framework for reducing the building energy performance gap

Abstract:
The performance gap between predicted and actual energy consumption in the building industry remains an unsolved problem in practice. This paper aims to minimize this gap by proposing a hybrid-model using building simulation and machine learning (ML) models inspired by the concept of time-series decomposition: 1. Using first-principles methods in different levels of information to convert the building discrete features and predictable patterns in time-series format. 2. Import the physical model’s output into the ML model as input. 3. Training the ML model to align the performance and calibrate the result. The approach is tested in the measured energy load from an office building in Shanghai. Hybrid-model shows higher accuracy in prediction with a better interpretation for gap magnitude investigation in building energy. In summary, the method demonstrates how domain knowledge via building simulation incorporated with data-driven methods, especially ML leads to improved predictions.

[Download paper here]

Citation: Chen, X., Guo, T., & Geyer, P. (2021). A hybrid-model forecasting framework for reducing the building energy performance gap. In 28th International Workshop on Intelligent Computing in Engineering, EG-ICE 2021. Berlin, Germany, 2021, special issue on Advanced Engineering Informatics.