Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Abstract

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability.

Simulation of a Building with Hourly and Daily Varying Ventilation Flow: An Application of the Simulink S-Function

“…It measures the variability in the errors between the reference and modelled temperature. Following ASHRAE recommendations, for the well-performing model, CV RMSE should not exceed 15% [63,105]. Hence, in all cases, the model performed with satisfactory accuracy.…”

“…This method is based on an equivalent thermal-electric network model of a building zone, which consists of five resistances and one capacitance (5R1C), as presented in Figure 1. It has been described in detail recently [60][61][62][63].…”

“…This method is based on an equivalent thermal-electric network model of a building zone, which consists of five resistances and one capacitance (5R1C), as presented in Figure 1. It has been described in detail recently [60][61][62][63]. Internal and solar gains are split into three parts, Φia, Φst, and Φm, connected to the indoor air (Ti), the central node (Ts), and the thermal mass (Tm) temperature nodes, respectively.…”