The Fresh Workplace: Promoting Health and Sustainability in Commercial Buildings Through Occupant Centered Hybrid Ventilation

Xinyi Song, PhD – Assistant Professor, School of Building Construction, Georgia Institute of TechnologyE-mail: Xinyi.song@design.gatech.edu

Introduction Model Predictive Control (MPC) Development Ambient Air Quality Analysis

Objectives And Framework

Climate Suitability Analysis

Interface-Based Implementation

To thoroughly investigate the potential of hybrid ventilation in commercial buildings across different US climates, and to develop reliable, occupant-centered design and operation strategies that maximize natural ventilation in the workplace without compromising indoor air quality (IAQ), energy efficiency, economics or human comfort.

§ Climate Suitability Analysis§ Ambient Air Quality Analysis§ Model Predictive Control Development§ Interface-Based Implementation

Natural Ventilation:

Ventilation is qualified as “natural” if it generates no energy consumption or noise from fans or blowers, but is achieved solely by thermal, wind or diffusion effects by opening vents such as windows and doors (Heiselberg, 2002).

§ Save energy (Chen et al. 2017)§ Reduce Sick Building Syndrome (SBS) (EPA, 2007)§ Improve employee productivity and satisfaction (Singh,

1996; Wargocki et al., 2000; Preziosi et al., 2004; Brightman, 2005)

Issues:§ Weather constraints§ Reliability of outdoor air ventilation rates, § Moisture control § Entry of outdoor air pollutants without filtration

Hybrid Ventilation:

Mixed use of natural ventilation and mechanical systems that include air distribution equipment and refrigeration equipment for cooling that could be switched or combined in response to internal conditions and the prevailing weather (Brager et al., 1998).

§ Improves the predictability and controllability of purely natural ventilation systems

§ provides a stimulating and pleasurable indoor environment where occupants have direct access to fresh air

Climate zones: Typical Meteorological Year (TMY) as weather input

https://cyberparent.com/green-building/climate-zones-map/

Baseline Building: Configuration of the Caddel Building and the construction of DOE medium commercial buildings

Generalizability:Test the impact on air exchange rate and average indoor temperature through the EnergyPlus airflow network

§ Building configurations,§ Important design parameters (e.g. building shapes and

window-wall ratio) and§ Ventilation types (single and cross ventilation)

Uncertainty Analysis: reduce the energy performance gap and thermal performance gap

§ Microclimate Uncertainty§ Urban Heat Island Effect, Local Wind Speed, Ground

Reflectance § Building Uncertainty

§ Interior and Exterior Convection, Material§ Operation Uncertainty

§ Occupant Presence/Electric Equipment/LightingThermal Standards:

§ Adaptive thermal comfort model in ASHRAE 55§ EN 15251 (CEN, 2007) with TM52 The Limits of Thermal

Comfort: Avoiding Overheating in European Buildings (Nicol, 2013)

§ 75% outdoor air relative humidity cut (WHO recommendation)

Climate Zone

Uncertainty AnalysisAnnual NV

suitable hours percentage

(Deterministic Simulation)

Difference of Annual

Percentage of hours

suitable for NV

Best Three Months for

Natural Ventilation

Number of months >

30% suitable NV hours on

average

Number of months <

10% suitable NV hours on

average

Mean percentage

of Annual NV suitable hours

Zone 1A - Mia Dec, Jan, Feb 1(31%) 7 12.10% 17.70% -5.60%

Zone 2A - Hou Mar, Apr, Oct 0(24%) 5 11.20% 13.30% -2.10%

Zone 2B - Phe Jan, Mar, Nov 1 (35%) 5 13.90% 14.20% -0.30%

Zone 3A- Atl Mar, Apr, Oct 0 (27%) 6 12.50% 15.30% -2.80%

Zone 3B - Veg Mar, Pro, Nov 2 (40%) 4 14.90% 13.20% 1.70%

Zone 3B - LA May - Jul 10(63%) 0 40.90% 36.60% 4.30%Zone 3C - SF Jul - Sep 6(52%) 2 28.90% 22.30% 6.60%

Zone 4A- Bal Apr, May, Sep 1(39%) 6 10.40% 10.80% -0.40%

Zone 4B - Alb Mar, Apr, Oct 0(29%) 4 14.70% 12.30% 2.40%

Zone 4C -Sea Jun - Aug 4(40%) 5 16.80% 13.40% 3.40%

Zone 5A - Chi May, Aug, Sep 1(33%) 7 11.30% 11.80% -0.50%

Zone 5B - Den May, Jul, Aug 0(27%) 6 10.80% 10.50% 0.30%

Zone 6A- Min May, Jun, Aug 0(28%) 7 11.10% 11.30% -0.20%

Zone 6B - Hel Jun-Aug 0(27%) 7 11.80% 11% 0.80%

Zone 7A- Dul Jun-Aug 0(25%) 7 8.80% 9.60% -0.80%

Current Practice:

§ Simple heuristic rules based solely on outdoor air temperature, wind speed, relative humidity or indoor environmental factors such as CO2 accumulation, etc.

§ Lacks the ability to proactively respond to changing factors

Current Research:

§ Overlooked parameters, such as building microclimate, building properties and building operations

§ Control algorithms were only tested for a short time, neglecting factors such as seasonality.

§ Existing data models could be optimized for faster computation

Model Predictive Control Development Process

Three Phase Light Weight Neural Network

Three Building Intelligence Levels§ I1 - RB (rule-based) control§ I2 - MPC (model predictive control) in building level§ I3 - MPC with zoned strategies (floor level)

City Intelligence Level

Mean percentage of Energy

Saving

Mean of Window Opening

Percentage

Percentage of Window

Opening with Uncomfortable

hours

Average T out of Bound

Max/Min Building

Zone T Out of Bound

LAI1 47.50% 39.30% 18.80% 1.10% 7.60%I2 39.20% 31.30% 9.60% 0.10% 3%I3 52.30% 36.20% 12.30% 0.20% 4.50%

SFI1 32.80% 23.50% 27.60% 0.80% 6.50%I2 33.20% 24.70% 15.20% 0.10% 3.80%I3 41.30% 27.80% 17.80% 0.40% 4.95%

SeattleI1 15.90% 13.90% 33.50% 1% 4.70%I2 17.40% 15% 18.60% 0.20% 2.80%I3 22.50% 16.30% 20.70% 0.40% 3.40%

AtlantaI1 18.10% 17.40% 36.10% 1.80% 6.30%I2 17.90% 14.90% 24.40% 0.30% 3.50%I3 23.30% 17% 24.90% 0.50% 4.20%

Quantify the influence of outdoor air pollutants on the natural ventilation usage in different location settings across US.

Interested Air Pollutants§ PM2.5, PM10 and Ozone

Data Collection§ Hourly records from EPA§ Site Number, Data Record Time, Measurement,

Measurement Method, State, County, Location SettingsData Size

§ 2 ~ 10 million records per year for different pollutantsData Cleaning

§ Exclude the data with missing recording time and location§ Exclude the monthly data with too many missing data (>5%

of all or 12 consecutive missing values) Data Interpolation for Air Pollutant Data

§ Linear interpolation is used considering the simple patterns of filtered data

Model Predictive Control Development Process

FreshAirAn interactive smartphone application called FreshAir will be developed incorporating user preferences. Similar to a weather app, FreshAir provides hyper-local hourly and daily natural ventilation forecasts to inform and guide users when it is appropriate to open/close windows through a push notification.