Procedures for calibrating hourly simulation models to measured building energy and environmental data

This paper discusses procedures for creating calibrated building energy simulation programs. It begins with reviews of the calibration techniques that have been reported in the previous literature and presents new hourly calibration methods including a temperature bin analysis to improve hourly x-y scatter plots, a 24-hour weather-daytype bin analysis to allow for the evaluation of hourly temperature and schedule dependent comparisons, and a 52-week bin analysis to facilitate the evaluation of long-term trends. In addition, architectural rendering is reviewed as a means of verifying the dimensions of the building envelope and external shading placement as seen by the simulation program. Several statistical methods are also presented that provide goodness-of-fit indicators, including percent difference calculations, mean bias error (MBE), and the coefficient of variation of the root mean squared error (CV(RMSE)). The procedures are applied to a case study building located in Washington, D.C. where nine months of hourly whole-building electricity data and site-specific weather data were measured and used with the DOE-2.1D building energy simulation program to test the new techniques. Simulations that used the new calibration procedures were able to produce an hourly MBE of -0.7% and a CV(RMSE) of 23.1% which compare favorably with the most accurate hourly neural network models (Kreider and Haberl, 1994a, b).