Influencing Factors on Air Conditioning Energy Consumption of Naturally Ventilated Research Buildings Based on Actual HVAC Behaviours
Abstract
:1. Introduction
2. Materials and Methods
2.1. Method for Collecting Data in the Case Building
2.2. A Quantitative Approach to Describing the Actual HVAC Behaviour
2.2.1. Division of the Characteristic Stages of Air Conditioning Usage
2.2.2. Generation of the HVAC Behaviour Decision Branch-Based Clustering Analysis
- Classifications are significantly different from each other;
- If the number of clusters is k + 1, there are 2 classifications with similar features.
2.2.3. Stochastic HVAC Sequence Generation Based on the Monte Carlo Method
- 1.
- The setting parameter D indicates the D-th day of the year, with an initial value of “1” for January 1 and so forth, corresponding to the date December 31 when D = 365;
- 2.
- Determine the characteristic stage of AC usage on Day D, and load n typical daily AC operation schedules for all patterns in this stage. The probability of occurrence corresponding to each typical schedule is , and . Generate random numbers according to the procedure shown in Figure 3, and decide and output the AC operation schedule for Day D according to the distribution interval where the fall point lies. Enter the decision process for the next day, i.e., D = D + 1, and perform step 3;
- 3.
- Check if D meets D > 365; if so, sequence generation is complete; otherwise, go back to step 2.
2.3. Method for Predicting AC Energy Consumption under Multiple Building Scenarios
2.3.1. Parametrised Translation of the Stochastic Sequences by Python Programming
2.3.2. Simulation of AC Energy Consumption Based on Different Behavioural Patterns by Applying jEPlus
2.4. Method for Analysing the Importance of the Factors Influencing AC Energy Consumption
2.4.1. Calculation of Importance Scores Based on Random Forest Regression
- 1.
- Build a regression tree model for each bootstrap sample set to predict the corresponding OOB, using Equation (1) to calculate the mean square of the OOB residuals, denoted MSE1, MSE2, ……, MSEk;
- is the actual value of the dependent variable (DV) in the OOB dataset,
- is the predicted value of the dependent variable (IV) in the OOB dataset taken from the regression model.
- 2.
- Randomly permute the independent variable among the k OOB samples to form a new OOB sample for testing. Predict the new OOB using a random forest regression tree and compute a mean square of residuals to obtain the matrix ;
- 3.
- Calculate the importance score of the independent variable using Equation (2).
2.4.2. Study of Main Procedures for AC Energy Conservation Based on Important Influencing Factors
- is the annual AC power consumption per unit area of the building;
- is the total area of the building;
- and are the annual heating load and cooling load of the building, respectively; and
- and are the energy conservation ratio in heating and cooling condition, respectively.
- is the total value of uncomfortable hours for the entire building;
- is the combined duration of AC operation for the entire building;
- n is the number of air-conditioned rooms in the building;
- is the area of the i-th air-conditioned zone;
- and are the total area of collective and individual offices, respectively;
- and are the average measured duration of AC operation in collective and individual offices, respectively;
- is the value of uncomfortable hours in the i-th air-conditioned room from the simulation; and
- is the value of uncomfortable hours as a percentage of the duration of AC operation.
3. The Stochastic Prediction Model of HVAC Behaviour Based on the Actual Operating Characteristics of Research Buildings
3.1. Quantitative Description of Actual HVAC Behavioural Characteristics
3.1.1. Typical HVAC Behavioural Patterns and Their Distribution Characteristics
3.1.2. Quantitative Description of the HVAC Behaviour Decision Branch
3.2. Simulation and Verification of the Stochastic Sequence of HVAC Behaviour
4. Construction of the Database of Multiple Scenarios Based on Simulation
4.1. Verification of the Simulated Energy Consumption of the Case Building
4.2. Description of the Coupling Patterns between NV and AC Operation
4.3. Determination of Initial Factors Influencing the AC Energy Consumption
5. Importance Analysis of Factors Influencing AC Energy Consumption
5.1. Importance Score Calculation Based on the Actual Operational Characteristics of HVAC Behaviour
5.2. Importance Analysis for Factors Influencing AC Energy Consumption
- 1.
- For factors extracted from the building design scheme
- 2.
- For factors extracted from the internal disturbance elements
- 3.
- For factors extracted from the adaptive behaviours
6. Discussion: Key Strategies for AC Energy Conservation Based on Important Influencing Factors
7. Conclusions
- The cooling/heating setpoint temperature, the air permeability, and the people density of collective offices are critical influencing factors with a relative importance greater than 10% based on both behavioural patterns. Therefore, energy conservation strategies based on these factors are a high priority.
- The relative importance and number of significant and limited factors are remarkably different depending on the behavioural patterns. The key strategies for energy conservation in similar naturally ventilated research buildings should be based on the cooling/heating setpoint temperature, the air permeability, the people density of collective offices, the coupling pattern between NV and AC operation, and the SHGC of the external window.
- The effect of implementing key strategies in the case buildings was simulated. Accordingly, the power consumption can be reduced from 52.37 kWh/(m2·a) to 34.03 kWh/(m2·a), while the uncomfortable hours as a percentage of the duration of AC operation decreased from 51.49% to 26.78%, thus achieving 35.02% AC energy savings while promoting thermal comfort.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Characteristic Stages of AC Usage | Duration Date | Average Daily Outdoor Temperature/°C | Daily Probability of AC Operation | Average Daily Duration of AC Operation/h | |||
---|---|---|---|---|---|---|---|
Collective Offices | Individual Offices | Collective Offices | Individual Offices | ||||
Cooling season | Mid-summer | Jun. 21~Sep. 10 | 29.33 | 92% | 73% | 11.52 | 5.83 |
Early and late summer | May 11~Jun. 20 Sep. 11~Sep. 30 | 24.09 | 80% | 60% | 7.94 | 3.60 | |
Late spring and early autumn | Apr. 21~May. 10 Oct. 1~Oct. 20 | 20.59 | 30% | 16% | 2.09 | 0.55 | |
Transitional period | Apr. 11~Apr. 20 Oct. 21~Oct. 31 | 17.67 | 16% | 6% | 0.76 | 0.17 | |
Heating season | Late autumn and early spring | Mar. 21~Apr. 10 Nov. 1~Nov. 20 | 15.49 | 27% | 4% | 1.65 | 0.27 |
Early and late winter | Feb. 11~Mar. 20 Nov. 21~Dec. 10 | 9.94 | 53% | 30% | 4.84 | 1.73 | |
Mid-winter | Dec. 11~Feb. 10 | 6.29 | 94% | 56% | 10.86 | 3.30 |
Room Type | Operating Conditions | The Characteristic Stage | Average Daily Duration of AC Operation/h | Average Daily Duration of Opening External Windows/h | ||
---|---|---|---|---|---|---|
Measured | Simulated | Measured | Simulated | |||
Collective offices | Cumulative annual duration/h | 2538.35 | 2525.06 | 2808.74 | 2809.84 | |
Cooling condition | Mid-summer | 11.52 | 11.63 | 7.01 | 6.95 | |
Early and late summer | 7.92 | 7.72 | 10.19 | 10.36 | ||
Late spring and early autumn | 2.09 | 2.14 | 12.72 | 12.61 | ||
Heating condition | Mid-winter | 10.86 | 10.85 | 5.04 | 5.15 | |
Early and late Winter | 4.84 | 4.75 | 6.73 | 6.51 | ||
Late autumn and early spring | 1.65 | 1.63 | 9.78 | 9.93 | ||
Individual Offices | Cumulative annual duration/h | 1035.82 | 999.20 | 4112.61 | 4089.38 | |
Cooling condition | Mid-summer | 5.83 | 5.62 | 8.39 | 8.45 | |
Early and late summer | 3.60 | 3.42 | 13.84 | 13.91 | ||
Late spring and early autumn | 0.55 | 0.50 | 15.21 | 15.42 | ||
Heating condition | Mid-winter | 3.30 | 3.11 | 10.34 | 10.53 | |
Early and late Winter | 1.73 | 1.62 | 12.55 | 11.96 | ||
Late autumn and early spring | 0.27 | 0.20 | 14.70 | 14.27 |
Operating Conditions | Measured Power Consumption/kWh | Stochastic Behavioural Pattern | Fixed Behavioural Pattern | ||
---|---|---|---|---|---|
Simulated Power Consumption/kWh | Error Rate | Simulated Power Consumption/kWh | Error Rate | ||
Heating condition | 79,429.27 | 75,265.37 | −5.24% | 97,376.41 | 22.60% |
Cooling condition | 85,005.37 | 87,179.47 | 2.56% | 79,187.40 | −6.84% |
Coupling Pattern | Parametric Description | Description for NV Sequences | |
---|---|---|---|
M0 | Stochastic behavioural pattern | A default pattern, reflecting the actual operational characteristics of NV according to measurements. | The sample NV sequence should be referred to Figure 8b,d. |
Fixed behavioural pattern | For , and . For , and . | On day D, the daily NV sequence is (0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0). | |
M1 | For , . For , . | On day D, the daily NV sequence is (0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0). | |
M2 | For , . For , . | On day D, the daily NV sequence is (1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1). | |
M3 | For , when , ; when , . | On day D, if the daily AC sequence is (0,0,0,0,0,0,0,0,1,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0), then the daily NV sequence is (1,1,1,1,1,1,1,1,0,0,0,0,1,1,0,0,0,0,1,1,1,1,1,1). | |
M4 | For , . For , when , ; when , . | On day D, if the daily AC sequence is (0,0,0,0,0,0,0,0,1,1,1,1,0,0,1,1,1,1,1,1,0,0,0,0), then the daily NV sequence is (1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1). |
Code | Initial Influencing Factor | Unit | Range of Values | |
---|---|---|---|---|
1 | Building orientation (northwards deflection) | [−90, 90] | ||
2 | External wall thermal insulation | - | {S,E,I,C} [Note 1] | |
3 | Heat transfer coefficient of | the external wall | W/(m2·K) | [0.15, 2.50] |
4 | the roof | W/(m2·K) | [0.15, 3.00] | |
5 | the internal wall | W/(m2·K) | [0.60, 5.00] | |
6 | the floor slab | W/(m2·K) | [0.60, 4.50] | |
7 | the external window | W/(m2·K) | [2.20, 6.40] | |
8 | Solar heat gain coefficient of the external window | - | [0.10, 0.85] | |
9–12 | Window-to-wall ratio of the east/south/west/north elevation | - | [0, 1] | |
13–16 | Sun-shading structure of the east/south/west/north elevation | - | {H,HS,HSL,L} [Note 2] | |
17–20 | Shading length of the east/south/west/north elevation | m | [0, 1.8] | |
21 | Air permeability performance | ac/h | [0, 2] | |
22 | People density in individual offices | m2/person | [12, 24] | |
23 | People density in collective offices | m2/person | [3, 9] | |
24 | Power density of lighting | W/m2 | [6, 18] | |
25 | Power density of equipment | W/person | [0, 300] | |
26 | Cooling setpoint temperature | °C | [18, 30] | |
27 | Heating setpoint temperature | °C | [18, 30] | |
28 | Coupling pattern between NV and AC operation | - | {M0, M1, M2, M3, M4} [Note 3] |
Code | Important Influencing Factor | Relative Importance | Importance Evaluation |
---|---|---|---|
1 | Cooling setpoint temperature | 100.0% | Type A—Critical factors |
2 | Air permeability performance | 67.0% | |
3 | Heating setpoint temperature | 46.6% | |
4 | People density of collective offices | 17.3% | |
5 | Coupling pattern between NV and AC operation | 7.8% | Type B—Significant factors |
6 | SHGC of the external window | 4.5% |
Code | Important Influencing Factor | Relative Importance | Importance Evaluation |
---|---|---|---|
1 | Air permeability performance | 100.0% | Type A—Critical factors |
2 | Cooling setpoint temperature | 82.2% | |
3 | Heating setpoint temperature | 43.8% | |
4 | People density of collective offices | 17.1% | |
5 | SHGC of the external window | 4.5% | Type B—Significant factors |
6 | Heat transfer coefficient of the external wall | 4.3% | |
7 | Heat transfer coefficient of the external window | 3.6% |
The Case Building | Original Values | Optimal Values | ||
Important influencing factors | Cooling setpoint temperature | Mid-summer | 25 °C | 25 °C |
Early and late summer | 25 °C | 25 °C | ||
Early autumn and late spring | 25 °C | 25 °C | ||
Heating setpoint temperature | Mid-winter | 26 °C | 24 °C | |
Early and late winter | 26 °C | 25 °C | ||
Early spring and late autumn | 26 °C | 24 °C | ||
Air permeability performance | 0.7 ac/h | 0.2 ac/h | ||
People density of collective offices | 6 m2/person | 6 m2/person | ||
Coupling pattern between NV and AC operation | Mid-summer | M0 | M4 | |
Early and late summer | M0 | M3 | ||
Early autumn and late spring | M0 | M3 | ||
Mid-winter | M0 | M1 | ||
Early and late winter | M0 | M1 | ||
Early spring and late autumn | M0 | M1 | ||
SHGC of the external window | 0.819 | 0.3 | ||
Simulated AC energy consumption | AC energy consumption for cooling | 28.10 kWh/(m2·a) | 23.75 kWh/(m2·a) | |
AC energy consumption for heating | 24.26 kWh/(m2·a) | 10.28 kWh/(m2·a) | ||
Total AC energy consumption | 52.37 kWh/(m2·a) | 34.03 kWh/(m2·a) | ||
Simulated thermal comfort | Uncomfortable hours as a percentage of the duration of AC operation | 51.49% | 26.78% |
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Wu, J.; Chen, S.; Ying, X.; Shu, J. Influencing Factors on Air Conditioning Energy Consumption of Naturally Ventilated Research Buildings Based on Actual HVAC Behaviours. Buildings 2023, 13, 2710. https://doi.org/10.3390/buildings13112710
Wu J, Chen S, Ying X, Shu J. Influencing Factors on Air Conditioning Energy Consumption of Naturally Ventilated Research Buildings Based on Actual HVAC Behaviours. Buildings. 2023; 13(11):2710. https://doi.org/10.3390/buildings13112710
Chicago/Turabian StyleWu, Jiajing, Shuqin Chen, Xiaoyu Ying, and Jinbiao Shu. 2023. "Influencing Factors on Air Conditioning Energy Consumption of Naturally Ventilated Research Buildings Based on Actual HVAC Behaviours" Buildings 13, no. 11: 2710. https://doi.org/10.3390/buildings13112710