Evaluation of whole-building energy baseline models for measurement & verification of commercial buildings

Abstract

This dissertation documents the research, development and application of whole-building energy baseline models for the determination of energy savings in commercial- and hospital buildings through the process of measurement and verification (M&V). This study compliments the principles addressed in resources such as SANS 50 010 and the International Performance Measurement and Verification Protocol (IPMVP). The methodology developed in this study requires fitting a baseline model to data from a baseline period and using it to predict what the energy consumption would have been during a subsequent period. The main focus is placed on finding/developing whole-building energy baselines with low uncertainty as it directly affects the size of the savings determined. It was also required to find baseline models that can be used to track performance on small time intervals. As the baseline model needs to adjust to changing conditions (energy governing factors), it was required to firstly determine the major energy governing factors in commercial buildings. From literature it was found to be ambient temperature as heating, ventilation and air conditioning (HVAC) systems are typically the largest energy consumers. However time-of-day also plays a vital role as occupancy differs throughout the day and different modes of HVAC operation may occur at different times of the day. Regression modelling is the most common method used to develop baselines for energy use. This creates a relation between the energy use and the energy governing factor. A regression model therefore expresses energy use as a function of the ambient temperature. This single regression of energy use versus ambient temperature delivers very poor results as it does not consider the time factor. The Day-Time-Temperature (DTT) model was the best performer found in literature. This model addresses both the influences of temperature and the time factor by introducing multiple linear regressions for specific time categories. By analysing the daily load profiles in building energy use, it is possible to identify the different load profiles at different times of the day, which is a good indication of the number of multiple regressions required. Analysing a complete year’s profile can become rather intensive especially if the load profiles aren’t constant throughout. For this reason it is more effective to create regression models for the smallest possible time categories, in this case hourly. In literature the DTT model categorized load profiles for each hour of each weekday type to produce 24 x 7 regression models. In this study the load profiles were categorised even further in the search for more accurate models. The load profiles were categorised for each hour of each day type per season (24 x 7 x 4 regression models) and also for each hour of each day type per month (24 x7 x12) as different energy use is expected for cooling and heating requirements each month or each season. The performance of the 3 DTT model variations were evaluated against a set of testing data from 5 buildings spanning a diversity of building types, climate zones and sizes. The first important finding from the results is that accuracy greatly increases by introducing the time factor. Better correlations were found when regressing in time categories during which only a specific heating or cooling energy is required. From the results, it is evident that the DTT 24x7x12 model performed the best when using the temperature data from which the regression model was developed, as input into each energy use function. This model performed poorer when using a completely new set of input data which indicates some model instability. Both the DTT 24x7x1 and the DTT 24x7x4 model remained stable when using a second set of data although the DTT 24x7x1 model fails to properly adjust to temperature variations due to the limitations of a linear regression that was applied to a polynomial correlation. This, of course, leaves room for future work to reduce the number of time categories and instead apply more complex regression models on identifiable cyclical patterns of energy use. In M&V practice the uncertainty of a baseline always refers to the uncertainty of a model with regards to the actual values from which that model was created, and therefore the uncertainties as determined from year 1’s data will typically be used to define baseline uncertainty and reduce the savings achieved. Therefore by knowing which models remain stable with the use of new data it is concluded that the best performing model was in fact the DTT24x7x4 model. This model performed second best in year one, but performed best during year two whilst being able to adjust well to temperature changes and still remain a stable model