1. Investigation into HVAC Energy Consumption in a Simplified Residential Building
Model through EnergyPlus Simulations
Colin Moynihan
10 November 2014

2. 2
Introduction
HVAC energy consumption is dependent on many different variables, including climate,
type or size of building, human comfort level, etc. In order to gain a better understanding of
HVAC energy consumption in residential buildings and to investigate the correlation, if any,
between outdoor temperature, human occupancy and HVAC energy consumption, a simple
house was modeled and simulated using EnergyPlus under three scenarios of varying HVAC
system operation conditions.
The modeled residential building is a one story, two zone house located in San Diego,
California, USA. This location was chosen because of firsthand experience and understanding of
the climate and residential buildings in San Diego, Ca. The HVAC system used is a fan coil
system with boilers and chillers. The model house was designed such that one zone isolates the
bedroom, while the remaining area is encompassed within the other zone. In the first
“Standard” scenario, the HVAC system will condition only the zone that is currently occupied.
The second “Intermediate” scenario will condition both zones while either zone is occupied. The
final “24/7” scenario will condition both zones regardless of whether or not the house is
occupied. Analyzing these three scenarios will allow an understanding of how outdoor
temperature and human occupancy affect HVAC energy consumption.
Methodology
During all three HVAC operation scenarios, certain assumptions and simplifications
were made. In order to remove the possibility of changing daily schedules from affecting the
HVAC energy consumption, the two occupants in this model have identical daily schedules.
When observing hourly HVAC energy consumption, weekends and holidays were ignored.
Naturally, weekend and holiday schedules would be radically different from a weekday schedule,
therefore ignoring them allows for hourly averaged HVAC energy consumption data and energy
consumption patterns to be more easily observed.
For all three simulations, the heating and cooling thermostat set points are fixed at
21.1°C and 23.9°C, respectively. The HVAC system and the set points were determined based on
that of the EnergyPlus idf example file Exercise 2A. These were chosen because the example file
also models a building meant for human habitation. These set points are assumed to be well
within the human comfort level, and do not vary while the house is occupied. This removes the
possibility of varying human comfort level from affecting HVAC energy consumption. Building
materials and compositions were also taken from the EnergyPlus idf example file Exercise 2A.
Furthermore, shading devices are placed on the windows to reduce direct incoming solar
radiation. This accounts for the lack of neighboring building or other environmental shading
objects that would normally be present in a residential area.
With San Diego weather data obtained from the U.S. Department of Energy website,
EnergyPlus simulations were run to obtain internal zone temperature and HVAC energy
consumption data. When analyzing seasonal variations between the three scenarios, the two
extremes are observed, summer and winter. Summer is made up of June, July, August, and
September, the months with the greatest average temperatures, while winter is made up of
January, February, March, and December, the months with the lowest average temperatures.
When taking hourly weekday averages, two arbitrary weeks were chosen from January and
August.
Simplification of the design and operation conditions, as stated above, are necessary to
isolate a few variables for analysis and to observe correlative patterns.

3. Linear (Summer - 24/7) Linear (Winter - 24/7)
Linear (Summer - Intermediate) Linear (Winter - Intermediate)
Linear (Summer - Standard) Linear (Winter - Standard)
Standard Scenario Intermediate Scenario 24/7 Scenario Outdoor Temperature
3
Results
60
50
40
30
20
10
Figure 1 .
Summer and Winter Linear Regression Lines for Energy Consumption versus Temperature in all HVAC Scenarios
Energy consumption versus average daily temperature were graphed (see Fig. A-1
through A-6) and using linear regression analysis, the best-fit lines in Fig. 1 were obtained. From
Fig. 1, clear correlations are observed. During the summer months, HVAC energy consumption
is positively correlated with outdoor temperature, whereas in the winter months, a negative
correlation is observed. Furthermore, during the winter months, the Standard scenario yields a
shallower slope,−0.70 푘푊ℎ⁄°C, than the Intermediate or 24/7 scenarios, which both have
similar slopes; −2.86 푘푊ℎ⁄°C and −2.83 푘푊ℎ⁄°C, respectively. During the summer months,
unlike during the winter months, the Standard scenario has a greater slope,5.76 푘푊ℎ⁄°C, than
the Intermediate scenario,4.88 푘푊ℎ⁄°C.
1200
1000
800
600
400
200
Figure 2.
Monthly HVAC Energy Consumption versus Outdoor Temperature
y = 6.68x - 111.67
y = -2.83x + 68.75
y = 4.88x - 84.65
y = -2.86x + 63.59
y = 5.76x - 100.12
y = -0.70x + 16.84
0
5 10 15 20 25 30
Energy Consumption (kWh)
Temperature (°C)
24
22
20
18
16
14
12
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Temperature (°C)
Energy Consumption (kWh)
Month

4. In Fig. 2, a clear pattern is observed where HVAC energy consumption is lowest for the
standard scenario, and highest for the 24/7 scenario, with the intermediate scenario residing
somewhere in between. However, this pattern does not hold true during the summer months of
June, July, August and September, where the HVAC energy consumption for the standard
scenario is either equivalent to, or greater than that of the intermediate scenario.
5
4
3
2
1
0
Energy Consumption (kWh)
Bedroom Occupancy Zone 1 Occupancy Standard Scenario
Intermediate Scenario 24/7 Scenario
Time
Figure 3.
Comparison of Hourly Weekday Energy Consumption for all three Scenarios, Jan.2-6
5
4
3
2
1
0
Energy Consumption (kWh)
Bedroom Occupancy Zone 1 Occupancy Standard Scenario
Intermediate Scenario 24/7 Scenario
Time
Figure 4.
Comparison of Hourly Weekday Energy Consumption for all three Scenarios, Aug.7-11
2
Occupancy (# people)
0
2
Occupancy (# people)
0
In Fig. 4, two characteristic spike in energy consumption are observed immediately
before and after the period of zone vacancy. All three scenarios reach roughly the same level in
their respective spikes. This spike is much less pronounced in Fig. 3.
Unlike in Fig. 3, the Standard Scenario energy consumption in Fig. 4 is greater than both
the Intermediate and 24/7 Scenarios before 9:00, and after 20:00.
4
Discussion
The negative correlation during the winter months in Fig. 1 shows that as temperature
decreases, HVAC energy consumption increases. The positive correlation during the summer
months shows that an increase in outdoor temperature results in an increased HVAC energy
consumption. This change in correlation is the result of the HVAC system heating versus

5. cooling. The negative correlation corresponds to HVAC heating the zones, and the positive
correlation corresponds to cooling the zones. The larger magnitude of the slopes for the summer
months implies that energy consumption is more sensitive to changes in temperature during
summer than winter, and that the HVAC system is more efficient at heating zones than cooling.
In Fig. 2, during the summer months the Standard energy consumption is observed to be
equal to or greater than that of the Intermediate scenario. This implies that during the months
with the highest average daily temperatures, the Intermediate scenario is more energy efficient
than the Standard scenario. The greater energy consumption for the Standard scenario seen in
Fig. 4 also illustrates this inefficiency in HVAC operation. From these observations, one can
infer that keeping both zones conditioned while the house is occupied is more efficient than
conditioning only the zone which is occupied.
The characteristic spikes seen in Fig. 4 are not associated with outdoor temperature (see
Fig. A-10). Instead they can be explained when looking at occupancy. As seen in Table A-1, the
occupants are scheduled to use the kitchen between 7:00 – 8:00, and again from 19:00 – 20:00.
Kitchen appliances, such as the oven and stove, radiate much heat and can increase the zone
temperature. Because this use of the kitchen results in heat radiation, the zone temperature
would increase, thus placing an increased demand on the HVAC system to condition the zone.
However, this large spike is only observed during the August week (Fig. 4) because the HVAC
system is cooling the zones, and increasing the zone temperature would result in an increased
demand. The lack of the same energy consumption spike during the January week (Fig. 3) can
be attributed to the fact that the HVAC system is instead heating the zones. Adding heat to the
zone would not have the same increasing demand effect on the HVAC system, in this case. From
these observations, it is reasonable to conclude that appliances, and thus their usage by humans,
play a large role in HVAC energy consumption.
5
Conclusion
HVAC energy consumption was investigated through EnergyPlus simulations of a model
residential building located in San Diego, Ca. Through these simulations, a negative correlation
between outdoor temperature and HVAC energy consumption was discovered during the winter
months, and a positive correlation was discovered during the summer months. This is attributed
to the fact that the HVAC system heats in the winter and cools during the summer. From linear
regression analysis, it was also found that energy consumption is more sensitive to outdoor
temperature changes during the summer, when the HVAC system cools zones.
The Standard scenario was found to be less efficient than the Intermediate scenario
during the summer. This was observed when monthly totals and weekday hourly averages were
taken for HVAC energy consumption. Further investigation would be necessary to determine
whether a scenario combining both the Standard and Intermediate scenarios would yield an
overall lower energy consumption than any one scenario individually.
From the large HVAC energy spikes found in the hourly averaged data, internal gains
were found to significantly contribute to HVAC energy consumption. Because internal gains are
dependent on usage and human occupancy, it is reasonable to assume that human behavior is
the key factor in determining internal gains’ contribution to HVAC energy consumption. Also,
appliance and lighting efficiency could also play a role in energy consumption. However, deeper
investigation would be needed to determine how either efficiency or human behavior impact
energy consumption.
Finally, zone humidity was neither controlled nor measured during these simulations. In
actuality, humidity plays a large role in human comfort level. Further study into zone humidity
control could yield an understanding of how HVAC energy consumption is affected.

6. HVAC Energy Consumption Linear (HVAC Energy Consumption)
8 10 12 14 16 18 20
Temperature (deg C)
HVAC Energy Consumption Linear (HVAC Energy Consumption)
15 17 19 21 23 25 27
Temperature (deg C)
HVAC Energy Consumption Linear (HVAC Energy Consumption)
8 10 12 14 16 18 20
Outdoor Temperature (deg C)
HVAC Energy Consumption Linear (HVAC Energy Consumption)
15 17 19 21 23 25 27
Outdoor Temperature (deg C)
6
Appendix
y = -0.70x + 16.84
20
15
10
5
0
Energy Consumption
(kWh)
Figure A-1.
Linear Regression Analysis of Standard Scenario Energy Consumption versus Outdoor Temperature in Winter
y = 5.76x - 100.12
80
60
40
20
0
Energy Consumption
(kWh)
Figure A-2.
Linear Regression Analysis of Standard Scenario Energy Consumption versus Outdoor Temperature in Summer
y = -2.86x + 63.59
60
40
20
0
Energy Consumption
(kWh)
Figure A-3.
Linear Regression Analysis of Intermediate Scenario Energy Consumption versus Outdoor Temperature in Winter
y = 4.88x - 84.65
80
60
40
20
0
Energy Consumption
(kWh)
Figure A-4.
Linear Regression Analysis of Intermediate Scenario Energy Consumption versus Outdoor Temperature in Summer

7. 80
60
40
20
HVAC Energy Consumption Linear (HVAC Energy Consumption)
Figure A-5.
Linear Regression Analysis for 24/7 Scenario Energy Consumption versus Outdoor Temperature in Winter
80
60
40
20
HVAC Energy Consumption Linear (HVAC Energy Consumption)
Figure A-6.
Linear Regression Analysis for 24/7 Scenario Energy Consumption versus Outdoor Temperature in Summer
Standard Scenario Intermediate Scenario 24/7 Scenario Outdoor Temperature
16
14
12
10
4.5
4
3.5
3
2.5
2
1.5
1
0.5
Figure A-7.
Comparison of Hourly Weekday Energy Consumption for all three Scenarios, versus Hourly Averaged Outdoor
Temperature, January 2 through January 6
7
y = -2.83x + 68.75
0
8 10 12 14 16 18 20
Energy Consumption
(kWh)
Outdoor Temperature (deg C)
y = 6.68x - 111.67
0
15 17 19 21 23 25 27
Energy Consumption
(kWh)
Outdoor Temperature (deg C)
8
0
Temperature (deg C)
Energy Consumption (kWh)
Time

8. Standard Scenario Intermediate Scenario 24/7 Scenario Outdoor Temperature
27
25
23
21
19
5
4
3
2
1
Figure A-8.
Comparison of Hourly Weekday Energy Consumption for all three Scenarios, versus Hourly Averaged Outdoor
Temperature, August 7 through August 11
8
Zone 1 Occupancy Bedroom Occupancy
Outdoor Temperature
2
30
25
20
15
Figure A-9.
Jan. 2 through Jan.6 Average Hourly Temperature and
Zone Occupancy
Zone 1 Occupancy Bedroom Occupancy
Outdoor Temperature
2
30
25
20
15
Figure A-10.
Aug. 7 through Aug.11 Average Hourly Temperature and
Zone Occupancy
Table A-1.
“ Cooking” Schedule Object fr om the
EnergyPlus Simulation Model Input Data File (idf)
17
0
Temperature (deg C)
Energy Consumption (kWh)
Time
0
10
Occupancy (# people)
Temperature (deg C)
Time
0
10
Occupancy (# people)
Temperature (deg C)
Time
Cooking
Fraction
Through: 12/31
For: WeekDays
Until: 7:00
0
Until: 8:00
0.2
Until: 19:00
0
Until: 20:00
0.5
Until: 24:00
0