Transfer-Learnt Models for Predicting Electricity Consumption in Buildings with Limited and Sparse Field Data

Buildings are a primary consumer of electricity in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building electricity consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art modeling techniques for building electricity consumption either rely on physics-based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While physics-based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building electricity consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from physics-based simulation frameworks to the field to improve the model accuracy, thus resulting in a physics-informed machine learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning-based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.