1. Estimation of HVAC energy saving potential in San Diego
apartment units through passive solar shading
Colin Moynihan
13 February 2015
Shimoda Laboratory
Osaka University, Graduate School of Environmental and Energy Engineering
______________________________________________________________________________
Abstract
______________________________________________________________________________
The aim of this paper is to estimate the HVAC energy saving potential in San Diego apartment
units through the use of passive solar shading devices. Through EnergyPlus simulations,
sensitivity analysis is performed for the length, angle, and height of a simple overhang shading
device attached to an average apartment model. A maximum HVAC energy consumption
reduction of 29.6 kWh and a peak demand reduction of 17.1 W are achieved during the summer
months. However, this summer reduction corresponds to an increase of 143.8 kWh in annual
HVAC energy consumption, and insignificant peak time energy demand reduction overall.
______________________________________________________________________________

2. 2
1. Introduction
Energy demand is an issue of increasing importance as population, and thus energy
consumption, increases. In 2010, the United States building sector accounted for about 40% of
the total energy consumption, of which about 55% is directly attributed to the residential
building sector [1, 2]. This increase in demand is especially important when considering the
increase of peak electricity demand, the maximum electricity demand that occurs during the day.
Although this peak demand can occur for only a small fraction of the day, it dictates the
maximum level that power plants must be able to operate at. In 2012, the peak demand in San
Diego reached a high of 4.6 GW [3]. Conventionally, additional energy sources are needed to
satisfy this increasing demand, often in the costly form of new power plants. More recently, large
emphasis is placed on reducing end-use energy consumption to lessen this need for new energy
sources. The San Diego utility company, San Diego Gas and Electric (SDG&E), recommends
retrofits for lighting and appliances to make them more energy efficient, or changing human
behavior such as adjusting thermostat settings or changing when and how often to use appliances
[4]. Furthermore, buildings in California are expected to meet stricter energy requirements set
forth by the California Energy Code, Title 24 [5]. Heating Ventilation and Air Conditioning
(HVAC) systems are of particular importance because they consume approximately 30% of
household energy consumption in California [6].
One strategy for reducing building HVAC energy consumption is passive solar designs.
These designs are used to manage solar heat gains so as to lessen or meet the heating and cooling
energy requirements for a building, while requiring little to no maintenance or mechanical
systems [7]. Bellia et al. (2013) described how passive solar shading devices contributed to an
annual lighting and HVAC energy savings of 20% for an office building in Italy [8]. While such
designs are well-studied, they are not widely implemented.
The aim of this paper is to estimate the HVAC energy saving potential in San Diego
apartment units through the use of passive solar shading devices.
2. Methodology
This investigation into energy savings potential is carried out in the following parts.
Firstly, an average apartment model is created from residential sector housing characteristics.
Simulations through EnergyPlus are conducted for this base case to observe thermal loads and
HVAC energy data. Secondly, the energy consumption in San Diego residential buildings is
investigated and compared to the data obtained from the base case simulation. Next, the shading
device is designed and added to the base case model. Sensitivity analysis is then performed for
the length, angle, and height of the shading device. Finally, HVAC energy data obtained from
the sensitivity analysis is compared to that of the base case to determine the energy savings
potential.
2.1 Average San Diego Apartment Model
Based on averages from data from the US Census Bureau and the US Energy Information
Administration, an average apartment model was created in EnergyPlus to represent the entire
apartment unit stock of San Diego (figure 1) [9, 10]. EnergyPlus is a whole building energy
simulation program which provides detailed building energy and thermal load data for analysis.

3. 3
Characteristic features of this model are reported in Table 1 below. It is important to note that all
insulation, fenestration, air change rates, building materials, etc. are in compliance with the
California Energy Code, and ASHRAE standards. Certain features, such as internal electrical
appliances and lighting loads, are taken from the EnergyPlus “RefBldgMidriseApartmentNew
2004_Chicago” IDF example file [11].
Floor Area (m2
) 110.6
Ceiling Height (m) 2.5
Wall Composition ½” gypsum
Steel-framed insulated walls
1” Stucco exterior
Shared Wall Composition ½” gypsum
Steel-framed insulated walls
½” gypsum
Ceiling / Floor Composition CP02 Carpet
MAT-CC05 4 HW Concrete
½” gypsum
HVAC System Ideal Loads Air System
Max heat/cool capacity autosized
Cooling Setpoints Night: 23.85°C / Day: 24.7°C
Heating Setpoints Occupied: 20°C / Unoccupied+Night: 17.2°C
Internal Gains People 3 occupants, Fraction Radiant: 0.3
Equipment 5.38W/m2
, Fraction Radiant: 0.5
Lights 3.88W/m2, Fraction Radiant: 0.7
Wall Heat Transfer Ceiling, Floor, East Wall, West Wall Adiabatic
South Wall, North Wall Outside Boundary Condition
Floor/Ceiling Insulation Weighted U-factor ≤ 0.039
Walls Insulation Weighted U-factor ≤ 0.105
Fenestration Specifications Weighted U-factor ≤ 0.46
RSGH ≤ 0.22
40%max Fenestration area/Conditioned floor area
Weather Data San Diego E+ data file
Infiltration Rate 0.75 ach
Continuous Exhaust Rate 0.04 ݉ଷ
‫ݏ‬ൗ
Intermittent exhaust Max 0.075 ݉ଷ
‫ݏ‬ൗ
Fig 1. Average San Diego Apartment Model Floorplan (internal walls not pictured)
Table 1
Average San Diego apartment model characteristics and simulation conditions [5, 10, 11, 12, 13, 14]

4. 4
Within this average apartment model, an EnergyPlus Ideal Loads HVAC system was
chosen to provide the heating and cooling requirements. This Ideal Loads system will satisfy the
HVAC requirements with 100% efficiency, with the maximum heating and cooling loads
determined by the autosizing feature of EnergyPlus. This system was chosen over others because
of its simplicity. The potential for HVAC energy savings is being analyzed, and using an ideal
system will provide the necessary HVAC energy data to make that assessment. After
determining the energy savings potential, different HVAC systems can easily be substituted into
EnergyPlus to achieve more specific results.
The ceiling, floor, and east and west walls of the model are assumed to be adiabatic to
simplify the simulations. This model represents the average of all apartment units in San Diego,
therefore is it reasonable to assume that the apartments directly neighboring it are identical. Thus
the indoor temperatures would be identical, resulting in no heat transfer through the walls of
neighboring apartments. Instead, they act as heat storage features.
2.2 San Diego Residential Energy
Analyzing data from San Diego Gas and Electric, San Diego’s utility company, an
average annual household HVAC energy consumption of 1700±300 kWh is determined [11].
This provided a target while designing the average apartment model to ensure that the
assumptions made would not significantly alter the energy consumption. The HVAC energy
consumption from the average apartment simulation is 1502kWh.
2.3 Passive Solar Shading Device
The passive shading device was designed to block sunlight from entering through
fenestration during the summer while the solar altitude angle is large, but to allow sunlight to
enter during the winter while the solar altitude angle is small (figure 2) [15]. For simplicity, an
overhang system was chosen to allow focus to be placed on key characteristics, such as angle,
height, and length of the device (figure 3).
Window
Fig 2. Passive shading device with shading variations
based on seasonal solar angles
Overhang Shading
Device
Fig 3. Shading device sensitivity analysis variables
Angle
Height

5. 5
0
10
20
30
-3
-2
-1
0
1
2
3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Temperature[degC]
EnergyConsumption[hourlykWh]
Time
Internal Gains Window Heat Gains
Wall Total Heat Load Floor Heat Load
Ceiling Heat Load Internal Walls Heat Load
Internal Mass Heat Load Infiltration Heat Load
Ventilation Heat Load Mech.Ventilation Heat Load
Total Average Thermal Load Outdoor Temperature
0
10
20
30
-3
-2
-1
0
1
2
3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Temperature[degC]
EnergyConsumption[hourlykWh]
Time
Internal Gains Window Heat Gains
Wall Total Heat Load Floor Heat Load
Ceiling Heat Load Internal Walls Heat Load
Internal Mass Heat Load Infiltration Heat Load
Ventilation Heat Load Mech.Ventilation Heat Load
Total Average Thermal Load Outdoor Temperature
Fig 4. January average hourly thermal loads and outdoor temperature
for the base case simulation
Fig 5. August average hourly thermal loads and outdoor temperature
for the base case simulation
2.4 Sensitivity Analysis
Using EnergyPlus, sensitivity analysis is performed to determine the effect that angle,
height and length have on seasonal HVAC energy consumption, as seen in figure 3 above. In
addition, the effect of the shading device on peak demand is also of particular importance
because a significant reduction in either value can lead to large energy savings if scaled to
encompass San Diego in its entirety.
To estimate the potential for HVAC energy savings, the results from the length, angle and
height sensitivity analysis were compared to the HVAC energy consumption and demand data of
the average apartment model without the shading device. Differences between summer and
winter are observed during the sensitivity analysis. Due to San Diego’s milder climate, summer
is defined as the months of April through September, and winter is defined as the months of
October through March. January and August are chosen when single winter and summer months
are observed because they have the lowest and highest temperatures of the year, respectively.
3. Results
3.1 Base Case Simulation Results
Figure 4 and 5 detail the thermal load components in the apartment unit for the base case
in January and August, respectively. Colored bars above the zero axis represent positive thermal
loads, while the bars below the zero represent negative thermal loads. The solid black line
illustrates the net thermal load. The dashed black line represents the outdoor temperature. The
grey bars in both figures are of particular interest because they represent the heat gains
contributed from incoming solar radiation through the windows. During the day, between
roughly 9:00 and 16:00, these heat gains contribute a significant amount to the overall zone
thermal load. This is especially true during the winter, when it acts to heat the zone air. Because
of the large role they play in the net thermal load during the day, altering these values could have
a large impact on the net thermal load, and thus on the HVAC energy consumption.

6. 6
4000
4050
4100
4150
4200
4250
4300
1300
1340
1380
1420
1460
1500
1540
0 0.25 0.5 0.75 1 1.25
JanuaryAveragePeakHVAC
EnergyDemand[W]
WinterHVACEnergyConsumption
[kWhper6months]
Shade Length [m]
Winter HVAC January Average Peak Demand
770
780
790
800
810
110
120
130
140
150
0 0.25 0.5 0.75 1 1.25
AugustAveragePeakHVAC
EnergyDemand[W]
SummerHVACEnergyConsumption
[kWhper6months]
Shade Length [m]
Summer HVAC August Average Peak Demand
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
HVACEnergyConsumption
(MWhper6mo)
Shade Length (m)
Summer HVAC Winter HVAC Without Shading Device
3.2 Sensitivity Analysis Results
Greatest results were achieved during the sensitivity analysis for the shading device
length. A maximum HVAC energy consumption reduction of 29.6 kWh and a peak demand
reduction of 17.1W were achieved during the summer season. However, this corresponds to an
increase of 173.4 kWh during the winter months, resulting in an overall 143.8 kWh increase in
annual HVAC energy consumption. Figure 6 shows the total HVAC energy consumption by
shade length. The black bars represent the HVAC energy consumption during the summer, and
the white bars represent the HVAC energy consumption during the winter. The red line indicates
the base case value. The annual increase is observed in figure 6 for all shade lengths.
Figure 7 shows the winter HVAC energy consumption (dotted line) and the January peak
HVAC energy demand (solid line) versus shade length. Figure 8 shows summer HVAC energy
consumption (dotted line) and the August peak HVAC energy demand (solid line) versus shade
length. From figures 7 and 8, a negative correlation is seen between shade length and HVAC
energy consumption/peak demand during the summer, and a positive correlation is seen during
the winter.
Fig 7. Winter HVAC energy consumption and peak demand versus shade length Fig 8. Summer HVAC energy consumption and peak demand versus shade length
Fig 6. Total HVAC energy consumption by season in relation to simulation without shading

7. 7
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
45 56.28 70 80 90 100 105 115 120 125 135 140
HVACEnergyConsumption
[annualMWh]
Shade Angle
Summer HVAC Winter HVAC Without Shading Device
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2 2.05 2.1 2.15 2.2 2.25 2.3 2.35 2.4 2.45 2.5
HVACEnergyConsumption
[annualMWh]
Shade Height [m]
Summer HVAC Winter HVAC Without Shading Device
When the maximum HVAC energy peak demand reduction is scaled to the size of San
Diego, that is to assume that all apartment units install shading devices, the resulting peak
demand reduction is 3.7MW.
The angle and height sensitivity analysis yielded summer HVAC energy consumption
reductions, but to a lesser degree than that of the shade length. There is a negative and positive
correlation between angle and HVAC energy consumption during the summer and winter,
respectively (Figures A1 and A2). For height, opposite correlations are seen; positive in the
summer and negative in the winter (figures A3 and A4).
However, as seen in figures 9 and 10, the overall annual HVAC energy consumptions
showed little change or increased compared to the base case without the shading device
(represented by the red line), a similar effect to that of shade length.
4. Discussion
The overall annual increase in HVAC energy consumption for all sensitivity analysis
cases is the result of significant increase in winter HVAC energy consumption. The shading
device successfully blocks incoming solar radiation from entering the windows during the
summer, thereby decreasing the load placed on the HVAC system. However, during the winter,
the shade contributes to solar radiation blockage, limiting the amount of solar energy that enters
the building. As seen in figure 4, solar radiation contributes a great deal to the overall thermal
load of the system during the winter when it heats the zone. Reducing this thermal gain thereby
increases the heating load placed on the HVAC system, resulting in an increase of consumption.
Therefore, in order for this shading device to be effective, window blockage during the winter
must be further reduced. Winter window blockage can be minimized by decreasing the length or
angle, or increasing the height of the shade, but at the cost of also decreasing shading in the
summer. So in order to achieve both minimal shading in the winter but maximum shading in the
summer, a variable shading device that could change its angle or length depending on the season.
The scaled peak demand reduction does not yield significant results. Comparing the
3.7MW scaled peak demand reduction to the 4.6GW peak demand observed in San Diego in
2012, the reduction is insignificant. Peak demand is difficult to measure and predict because of
the variables it is dependent on. Peak demand is heavily dependent on occupancy, and occupant
behavior. Therefore, peak demand can vary greatly from household to household. For this
Fig 9. Total HVAC energy consumption versus shade angle
compared to consumption without shade
Fig 10. Total HVAC energy consumption versus shade height
compared to consumption without shade

8. 8
reason, the scaled peak demand reduction above is not an accurate nor precise quantity. Here it is
merely a rough estimation to determine the significance of the peak demand reduction achieved
for the average apartment model.
It is important to note that while the window U-factor was in compliance with Title 24,
sensitivity analysis was not performed on it. The U-factor determines the rate at which heat is
lost through the window and thus could play a significant role in limiting the amount of heat lost
during the winter, possibly minimizing the negative effect caused by winter window shading
[16].
5. Conclusion
Passive solar shading can reduce the HVAC energy consumption and peak energy
demand during the summer months, but causes an increase in consumption and peak demand
during the winter. The result is an overall increase of the annual HVAC energy consumption. It
is thus concluded that the passive solar shading design presented here is not enough to reduce the
annual HVAC energy consumption nor peak energy demand in San Diego apartment units.
However, this does not rule out the possibility of achieving positive results when used in
conjunction with other passive designs.
There are various passive solar design options that could result in an overall reduction of
HVAC energy consumption, such as increasing zone thermal mass, or thermal cooling chimneys.
Furthermore, there are emerging technologies that can be applied to building applications, such
as Phase-Change Materials [17]. Additionally, changing the design of the window or shade itself
could also increase its effectiveness. This can be achieved as mentioned above, or by changing
the actual style of shade. For example, slats as opposed to an overhang, as seen in one case in the
study performed by Bellia et al [8]. However, further investigation would be necessary in order
to determine the effectiveness of such designs and design combinations.

9. 9
Appendix
770
775
780
785
790
795
800
805
810
110
115
120
125
130
135
140
145
150
30 60 90 120 150
AugustAveragePeakTime
EnergyDemand[W]
SummerHVACEnergy
Consumption[kWhper6months]
Shade Angle [degrees]
Summer HVAC August Peak Time Demand
4000
4050
4100
4150
4200
4250
4300
1300
1340
1380
1420
1460
1500
1540
30 60 90 120 150
AugustAveragePeakTime
EnergyDemand[W]
WinterHVACEnergy
Consumption[kWhper6months]
Shade Angle [degrees]
Winter HVAC January Peak Time Demand
770
775
780
785
790
795
800
805
810
110
115
120
125
130
135
140
145
150
1.9 2 2.1 2.2 2.3 2.4 2.5 2.6
AugustAveragePeakTimeEnergy
Demand[W]
SummerHVACEnergyConsumption
[kWhper6months]
Shade Height (m)
Summer HVAC August Peak Time Demand
4000
4050
4100
4150
4200
4250
4300
1300
1340
1380
1420
1460
1500
1540
1.9 2 2.1 2.2 2.3 2.4 2.5 2.6
JanuaryAveragePeakEnergyDemand
[W]
WinterHVACEnergyConsumption
[kWhper6months]
Shade Height (m)
Winter HVAC January Peak Time Demand
Fig A1. Summer HVAC energy consumption and
peak demand versus shade angle
Fig A2. Winter HVAC energy consumption and
peak demand versus shade angle
Fig A3. Summer HVAC energy consumption and
peak demand versus shade height
Fig A4. Winter HVAC energy consumption and
peak demand versus shade height

10. 10
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