Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

The current Building Energy Performance Simulation (BEPS) tools are based on first principles. For the correct use of BEPS tools, simulationists should have an in-depth understanding of building physics, numerical methods, control logics of building systems, etc. However, it takes significant time and effort to develop a first principles-based simulation model for existing buildings—mainly due to the laborious process of data gathering, uncertain inputs, model calibration, etc. Rather than resorting to an expert’s effort, a data-driven approach (so-called “inverse” approach) has received growing attention for the simulation of existing buildings. This paper reports a cross-comparison of three popular machine learning models (Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Process (GP)) for predicting a chiller’s energy consumption in a virtual and a real-life building. The predictions based on the three models are sufficiently accurate compared to the virtual and real measurements. This paper addresses the following issues for the successful development of machine learning models: reproducibility, selection of inputs, training period, outlying data obtained from the building energy management system (BEMS), and validation of the models. From the result of this comparative study, it was found that SVM has a disadvantage in computation time compared to ANN and GP. GP is the most sensitive to a training period among the three models