Evaluation of simplified models for predicting CO2 concentrations in 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.
References (26)
- et al.
Effect of energy conservation by controlled ventilation: case study in a department store
Energy and Buildings
(1979) - et al.
Ventilation for control of indoor air quality: a case study
Environment International
(1982) Control of ventilation to conserve energy while maintaining acceptable indoor air quality
ASHRAE Transactions
(1976)- et al.
Impact of demand-controlled and economizer ventilation strategies on energy use in buildings
ASHRAE Transactions
(1999) - et al.
Literature review on CO2 based demand controlled ventilation
ASHRAE Transactions
(1997) - Emmerich SJ, Persily AK. State-of-the-art review of CO2 demand controlled ventilation technology and application....
- et al.
Experimental evaluation of CO2-based demand-controlled ventilation strategies
ASHRAE Transactions
(2002) - et al.
Assessing CO2 control in retrofits
ASHRAE Journal
(2002) - et al.
Application of CO2 based demand controlled ventilation using ASHRAE Standard 62: optimizing energy use and ventilation
ASHRAE Transactions
(1998) - et al.
Ventilation air flow measurement for ASHRAE Standard 62-1989
ASHRAE Journal
(1992)
Strategies for outdoor airflow control from a systems perspective
ASHRAE Transactions
Simulation results and analysis of eight ventilation control strategies in VAV systems
ASHRAE Transactions
Cited by (32)
Predicting the CO<inf>2</inf> levels in buildings using deterministic and identified models
2016, Energy and BuildingsCitation Excerpt :Lu et al. [24] used measured CO2 concentrations to accurately quantify number of occupants and air change rate, the latter only during unoccupied hours. Although the above studies have investigated the potential of incorporating CO2 in building energy and indoor air quality management, there are still very few examples in the literature concerning CO2 concentration prediction, like the one conducted by Lawrence and Braun [22]. Their application however was a tool to evaluate DCV retrofit savings rather than an optimisation tool for predictive control.
A novel complex air supply model for indoor air quality control via the occupant micro-environment demand ventilation
2016, Chaos, Solitons and FractalsA case study: The energy performance gap of the Center for Interactive Research on Sustainability at the University of British Columbia
2015, Journal of Building Engineering