Building Performance Evaluation and Tracking Tool

O. Sezgen, B. Smith*

*Indoor Environment Program, Energy & Environment Division, LBNL.

Numerous proxies have traditionally been used to measure the energy performance of heating, ventilating, and air conditioning (HVAC) systems in buildings. These include equipment coefficient of performance (COP) at standard conditions, annual integrated part load efficiency, and seasonal integrated part load efficiency. These proxies describe the performance of a building HVAC system in a limited fashion. More detailed information about performance must involve the dynamic behavior of the HVAC/building variables.

Measured HVAC time-series data are descriptive of the performance, but only under the strict boundary conditions that the building was exposed to during the monitoring (e.g., the weather conditions during monitoring, the control strategies that were applied during the same period, etc.). When one wants to estimate performance under other possible conditions, measured data is of limited use. A dynamic model of the building/HVAC system calibrated to monitored data, on the other hand, can facilitate such estimations.

Our objective in this project was to use a dynamic model of the HVAC system to develop a methodology for carrying the performance-related information from the design phase to the commissioning phase, operations phase, and even a retrofit phase.

In our methodology, during the design phase the HVAC model is built using the design documents and manufacturer-supplied data on equipment performance. At this stage, it is possible to emulate several design options (such as different equipment sizes, efficiencies, etc.), to compare the energy performance of these different options and to feed information back to the design process.

During the commissioning phase, the model built using the design data is calibrated to represent the dynamic behavior of the system as it actually performs. For this purpose, after the acceptance of the building, time-series data on the HVAC variables are used to revise the model parameters. At this point, the emulation results and the real data from the building should be very close.

We developed application software that allows the use of the above calibrated model in numerous ways during the operations and retrofit phases. The Building Performance Evaluation and Tracking Tool can be used for performance tracking, for analysis of different control strategies, and also for the analysis of different options during the retrofit phase.

Using the performance tracking options, data from the building can be compared to benchmark data from other similar buildings, to historic data from the same building during other time periods, or most significantly, to the simulated data using the HVAC model. Deviations in the building data from the simulated data may indicate problems in the HVAC system. The Figure shows the simulated and measured data for the chillers of a case-study building. This project, at least at this stage, is not aimed at pinpointing the source of such problems. In other words, it is not intended to be a diagnostics application, although this is a fertile research area.

Control strategy analysis options facilitate changes to the control logic actually used during the measurement and comparison of the emulated results to the actual measured data. The environmental conditions and the building loads are maintained at the levels that they occurred, but changes are made to the control choices such as temperature set points, or equipment status. Although at this stage, these control strategy analysis options serve as a "what-if" type of analysis facility, capabilities can be expanded to include optimization. In such an application, the tool would come back with the optimal set of choices for all of the control options.

Figure. Measured and simulated data for the chillers of a case-study building.

Finally, longer term actions can be analyzed using the retrofit analysis options of the tool. Here, changes that would require implementation of new equipment and hardware are analyzed. A typical example would be a chiller replacement project. Using the retrofit analysis options of the tool, one can compare the performance of the overall system under different chiller sizing and efficiency choices.

We used the Simulation Problem Analysis Research Kernel (SPARK) to build our emulation model. SPARK was developed by the Simulation Research Group of LBNL's Building Technologies Program. SPARK can be viewed as an object-based differential/algebraic equation solver. The models are represented as mathematical graphs (as opposed to linear data structures) in SPARK, and this feature facilitates emulation of sub-models without substantial changes to the initial model. This is also a crucial feature which facilitates emulations using changes in control strategies.

During the first year of this project, we focused our attention on chillers. This was mainly for demonstration purposes and this setup served as a test bed for the development of the Performance Evaluation and Tracking Tool. Clearly, optimization of chiller performance cannot be done independent of the effects of such action on the energy performance of the rest of the system. For example, reducing the condenser inlet temperature reduces the chiller electricity use, but increases the energy consumption of the cooling towers. Having demonstrated the concepts on chillers, we are now expanding our models to include first cooling towers and then cooling coils, and possibly the air distribution system.

| Return to Contents | Return to Previous Paper | Next Paper |