Los Angeles: Natural Ventilation Potential
from Present-Day to 2080
Year: 2024
Course: Data Science for Environmentally Responsive Buildings
Instructors: Elence Xinzhu Chen, Sunghwan Lim
Team: Sara Segura, Ryan Otterson
This experiment calculated the natural ventilation potential for a generic room in Los Angeles for both the current and
future climate. The room measures 6 x 6 x 4 meters, with
two 2-square-meter windows on the east and west facades.
Using a polynomial regression model based on 2020 climate
data, researchers predicted cooling loads for 2080, enabling
two approaches to assess ventilation potential. The first
modified window openings hourly for optimal comfort,
achieving 4,684 hours of natural ventilation out of 7,122 cooling
hours. The second predictive approach increased this to
4,779 hours, a 2% rise.
This experiment underscores the importance of natural ventilation for cooling enclosed spaces as temperatures rise. Cities
like Los Angeles can expect higher future temperatures,
with natural ventilation becoming insufficient for cooling
even in mild climates. While responsive window operability
can provide sufficient cooling for most hours, it cannot fully
compensate for the projected temperature increases, leading
to higher energy demands. This contradicts climate change
mitigation efforts and may overwhelm energy grids already
struggling, especially in cities like California. The inability to
provide passive cooling could leave vulnerable populations
even more at risk. Increased cooling loads will further exacerbate
global inequities in both the developed and developing
world.
Los
Angeles Weather Data
Los Angeles was chosen as the site of study
for its Mediterranean climate, reflected in the current weather data. As a city built within a desert, the
researchers anticipated its cool nights and mild temperatures to be ideal for
natural ventilation. The city experiences hot summers with low relative
humidity, and brief periods of high humidity in the winters as a rainy season.
Its humidity conditions were also promising for natural ventilation potential, as
high relative humidities in summers could have a compounding effect on
temperature in other cities. As a coastal city, Los Angeles’ wind speed and
clear wind direction were expected to produce positive effects on natural
ventilation potential (Figure 1).
After simulating the changed weather
conditions of Los Angeles in 2080, a sharp increase in dry bulb temperature was
observed year-round, especially in the summer months (Figure 2, 3). Relative
humidity remained consistent primarily in the summer months but dropped between
5-10% during the winter months (Figure 4). Solar radiation total kWh increased
by 5.1% in 2080. Wind factors remained constant or only slightly changed.
Figure 1: Wind rose for Los Angeles based on existing weather data (2020).
Figure 2: Dry-Bulb Temperatures and Relative Humidity
(2020 and 2080 Values)
Figure
3: Dry-Bulb Temperatures (2020 and 2080 Values)
Figure
4: Current and 2080 Relative Humidity in Los Angeles
Calculating
NV Potential
This experiment calculated the natural
ventilation potential for a generic room in Los Angeles for the city’s current
and future climate. The box-shaped room measures 6 x 6 x 4 meters, with two
windows on the east and west facades, each measuring 2 squared meters. Natural
ventilation availability was calculated using a discharge coefficient of 0.6
and a pre-set list of pressure coefficient values based on wind directions
(Figure 5). Wind speeds were directly pulled from weather data. Indoor target
temperatures were calculated based on the prevailing mean outdoor temperature
for each calendar month, with dry-bulb temperatures provided by the weather
data standing in for operative temperatures. These indoor target temperatures
and their respective natural ventilation requirements were calculated to
achieve optimal thermal comfort, as defined and measured by ASHRAE-55. Window
openings were adjusted on an hourly basis to achieve the target temperature, as
actual wind speeds fluctuated and wind directions changed air pressure values.
The final values for each category are shown in Figure 6. As shown in Figure 7, the unit was cooled
using natural ventilation for 1,711 hours out of the 1,920 total cooling hours
of the year, representing more than 89% of the cooling hours. Heating loads and
cold discomfort were not calculated at this phase of the experiment.
Figure
5: Pressure coefficients for windows on the west and east facades
Figure
6: Statistical summary of calculations for current weather conditions in Los
Angeles
Figure
7: Cooling Hours vs Natural Ventilation Hours for Current Weather
Model
Testing & Selection
The researchers tested three machine-learning
methods for predicting sensible cooling loads: a linear regression model, a
polynomial regression model, and a KNN model. The data from Figure 6 was
modified to remove sensible cooling loads and split into training and testing
sets (75% to 25%, respectively) for each model. Multiple input variable
combinations were tried and selected to achieve the lowest mean squared error
in the testing dataset, shown in Table 1. In the linear and polynomial
regression models, the variables chosen were dry-bulb temperature, wind speed,
and solar radiation for their correlation values and their resulting mean
squared errors. The KNN model was limited to using only dry-bulb temperature as
its sole input. The polynomial model was ultimately selected for its superior
accuracy and reduced mean squared error.
Future
Predicted Cooling Loads
Knowing its superior accuracy above the other
models, the polynomial regression model was used to predict the sensible
cooling loads for the 2080 climate. As anticipated by the increase in dry-bulb
temperatures, monthly aggregates of cooling loads jumped significantly between
2020 and 2080 (Figure 8, 9). While the absolute number of cooling hours
increased primarily in the summer months, the predictions reveal the starkest
relative increase in cooling hours during the late fall and winter months
(i.e., November through March). Strikingly, August cooling hours increased by
more than 250,000 J/S while December cooling hours increased by 2,157%.
Having the sensible cooling loads from the polynomial
regression allowed for the same calculations to be undertaken as with the 2020
weather data. Mean monthly temperatures, indoor target temperatures, natural
ventilation availability, natural ventilation requirements, and respective
window openings as area measurements were added. Natural ventilation hours were
then calculated from this information (Figure 10, 11).
Figure 8: Monthly Aggregated Hours of Sensible Cooling
Loads (> 0 J/S)
Figure
9: Comparison of 2020 and Predicted 2080 Cooling Loads
Figure
11: Monthly NV Hours: Current and 2080 Prediction
Figure 10: Statistical Summary of Calculations for
2080
Future
NV Potential: Two Methods
Two methods for cooling were employed. The first
method modified window openings to meet the current cooling loads for each hour
of the year, adapting to the changing wind speeds, wind directions, and air
pressures. Consequently, the unit was cooled using natural ventilation for
4,684 hours out of the 7,122 total cooling hours of the year. The second
approach involved pre-cooling the space by anticipating hours in which the
natural ventilation availability would be insufficient to satisfy their
requirements or cooling loads would be high (i.e., greater than 500 J/S). Its
logic is diagrammed in Figure 12. This predictive cooling method allowed for
4,779 hours of natural ventilation rather than 4,684, representing a 2%
increase (Figure 13). In both scenarios, natural ventilation was able to
compensate for a majority of the additional cooling hours in 2080. However,
their respective percentages relative to the total cooling hours each month
were lower in 2080 than in 2020 (Figure 11).
Figure
12: Predictive Cooling Algorithm Logic
Figure
13: Natural Ventilation Hours in 2080 with and without Predictive Cooling
Discussion
& Conclusion
This experiment speaks to the importance of
natural ventilation as a means of cooling enclosed spaces in both the short and
long term. Cities like Los Angeles can anticipate much higher temperatures in
the years to come. There is already a significant global discrepancy between
those that can provide cooling using air conditioning equitably and meet their
local energy demands. As shown in the 2080 natural ventilation calculations,
this experiment reveals a significant increase in the number of hours in which
natural ventilation will not suffice for cooling indoor spaces, even in cities
with mild climates. While responsive window operability to maximize thermal
comfort can provide sufficient cooling for a majority of the future hours, it
cannot compensate proportionally for the increase in temperature predicted for
the coming decades. This marks a sharp increase in the energy demands for
providing comfortable spaces in the future. Not only does this contradict the
global mandate for climate change mitigation, but also it may prove infeasible
for energy grids that are already struggling to meet energy demands. Despite
its global status as a cosmopolitan city, Los Angeles is already faced with
frequent power outages. The inability to provide passive cooling could render
already-vulnerable populations significantly more vulnerable in the coming
decades. As the results of this experiment illustrate, increased sensible
cooling loads can exacerbate global inequities and increase heat vulnerability
in both the developed and the developing world.