Elsevier

Building and Environment

Volume 41, Issue 2, February 2006, Pages 184-194
Building and Environment

Evaluation of simplified models for predicting CO2 concentrations in small commercial buildings

https://doi.org/10.1016/j.buildenv.2005.01.003Get rights and content

Abstract

Evaluation of a building for application of demand-controlled ventilation (DCV) typically involves the use of computer simulations to predict energy use/costs for both fixed ventilation and ventilation adjusted to maintain fixed CO2 levels within the space. The simulation tools incorporate models for predicting CO2 concentrations in response to internal sources (people), infiltration/exfiltration, and ventilation. This paper presents a detailed evaluation of different modeling approaches for predicting levels of CO2 in occupied spaces for small, single-zone commercial buildings employing packaged air-conditioning equipment. Two-zone and three-zone transient models were compared with a quasi-static equilibrium model applied to three distinctly different building types. Baseline data were derived from computational fluid dynamic models that were developed for field sites. A complete building system simulation model was then used to compare the impact of the different modeling approaches on the predicted energy cost savings associated with application of DCV in each building type. The use of a transient CO2 model did not have a significant impact on model prediction accuracy and energy cost savings predictions as compared with the quasi-static model. The difference in predicted annual energy costs between the various CO2 modeling types were small and less than might result from errors introduced by factors such as CO2 sensor uncertainty. Therefore, the use of an equilibrium model is sufficient for use in evaluating DCV for small commercial buildings.

Introduction

Demand-controlled ventilation (DCV) is a method used to minimize the energy penalty associated with providing appropriate ventilation for removing odors and contaminants within a room. With DCV, the amount of ventilation air is adjusted according to the occupancy level. Typically, CO2 is used as a passive tracer gas to determine human occupancy in the space, as originally proposed by Kusuda [1]. The best potential applications for DCV are rooms with highly variable occupancy, such as restaurants, stores, or auditoria [2].

Published studies going back to the 1970s (for example, [3]) have indicated the potential for energy savings with the application of ventilation control based on occupancy or CO2 levels. Emmerich and Persily [4], [5] provided a thorough review of the published studies, summarizing the application of DCV in approximately 20 field studies, plus other published papers and standards on simulation studies and sensor locations.

Alalawi and Krarti [6] presented a laboratory study comparing the effect of different feedback control strategies on the CO2 levels and HVAC equipment energy consumption. Schell and Smith [7] outlined the various control system decisions needed to retrofit CO2-based DCV in larger scale buildings and presented a case study that discussed the detailed modifications needed for a retrofit at one office building. Their discussion was based in part on earlier work [8] which described the use of the upper level CO2 set point in a proportional control strategy as an anchor point for evaluating zone ventilation rates. This work also outlined a method to estimate actual ventilation for an occupied space based on CO2 levels and known occupancy.

Most of the published studies and proposed analysis methods have assumed perfect mixing in the room under consideration, for example [9], [10], [11], [12], [13], [14]. The perfect mixing assumption simplifies the modeling of room CO2 concentrations, but at the expense of accuracy. Knoespel et al. [15] developed a pollutant transport model for analyzing the rate of change of contaminant concentrations in a building with multiple zones. O’Neill and Crawford [16] used the same model format to develop an inverse model for determining interzonal airflows and ventilation effectiveness from experimental data. Federspiel [13], [14] used a similar model with the assumption of perfect room mixing and developed a method for recursively estimating the source strength when some of the transport parameters are unknown.

Persily [17], [18] noted that the assumption of equilibrium conditions for estimating ventilation rates using mass balance equations may not be valid. For example, one particular study was described where air ventilation rates would be overestimated by a factor of two if equilibrium conditions were assumed to exist when in fact they did not. Under constant occupancy conditions, the time needed for a building or room to reach equilibrium is a function of the air exchange rate. A time period with stable occupancy and ventilation rate equal to three times the room time constant is required for room CO2 levels to reach 95% of their steady-state value [19]. Thus, in a room with a low air exchange rate, it may take up to 12 h of constant occupancy for equilibrium conditions to be reached [20]. Such a condition would exist for offices set to older ventilation standards of around 170 l/min per person, or 0.25 air changes per hour in the building measured. Persily also specified a criterion for a building to be considered in equilibrium [18].

The evaluation of a site for DCV retrofit should ideally involve the use of a simulation model that estimates energy savings as compared with fixed ventilation rates. Fig. 1 depicts the simulation process, inputs, and models needed to perform this evaluation. The goal of the work described in this paper was to identify an appropriate model for predicting space CO2 concentrations to be used in analyzing DCV retrofits for small commercial buildings having packaged HVAC equipment. The existing approaches typically use quasi-static models and no previous study has evaluated the effect of modeling approach on energy cost savings predictions.

Section snippets

Methodology

The process of identifying appropriate modeling approaches was carried out in several steps. Different field sites were identified that have a range of different occupancy patterns. Computational fluid dynamics (CFD) models were prepared for the sites in order to provide detailed benchmark data for evaluating the ability of simplified models to predict transient variations in space CO2 levels under realistic operating conditions. Transient and quasi-static models of the CO2 distribution were

CO2 model training results

Table 2, Table 3, Table 4 summarize model training results for the various formats studied and applied to the base case at each site. Based on the RMSE, the trained models all performed well. This conclusion is also supported by the plots of Fig. 7, Fig. 8, Fig. 9 for the school, restaurant and retail store sites, respectively. These plots show the ability of each model form to predict CO2 concentration transients for the base case simulated day as compared with the CFD predictions. For

Evaluation of CO2 model formats using a building system simulation

Even though different CO2 modeling approaches may give different CO2 levels, especially during periods of rapid or significant changes in occupancy, the impact of these differences on the overall cost savings estimates when evaluating DCV control may be relatively small. If this were the case, then the simplest modeling approach would be the most appropriate choice. The practical significance of using a transient CO2 model or the more simple quasi-equilibrium approach was studied by comparing

Conclusion

The use of a transient CO2 model is not necessary for evaluating cost savings associated with DCV for small commercial buildings. The errors are less than those that would occur due to other uncertainties such as CO2 sensor errors. One of the most important factors impacting DCV savings is the occupancy schedule. A companion paper addresses the issue of identifying site-specific occupancy schedules from field measurements using a quasi-equilibrium model and parameter estimation [26].

Acknowledgments

This research was supported in part by the California Energy Commission Pubic Interest Energy Research (PIER) Building Energy Efficiency Program.

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