A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy
Abstract
:1. Introduction
1.1. Research Background
- (i)
- innovative strategies for energy conservation coupled with indoor comfort improvement [4];
- (ii)
- (iii)
- elaboration of new high performing renewable energy plants, i.e., PV panels, to optimize the building-plant integrated system performance and environmental impact [7];
- (iv)
- increasingly detailed tools for simulating the thermal-energy performance of buildings and their components with respect to specific climatological contexts [8];
- (v)
- development of the building occupants’ awareness and its effect in energy saving [9];
- (vi)
1.2. Motivation
1.3. Purpose of the Work
2. Methodology
2.1. Workflow of the Activities
Sequence | Description of the phases |
---|---|
1 | Choice of the appropriate building to achieve interesting through post-occupancy evaluation for energy savings [29]; |
| |
2 | Analysis of the building design documents to evaluate architectural and technological properties (i.e., HVAC, lighting, heat water production, equipments data); |
| |
3 | Energy modeling and year-round dynamic simulation of the building base-case scenario (Scenario 0); |
| |
4 | In-field analysis campaign: indoor environmental measurements, occupants’ surveys and interviews following the approach in [23]; |
| |
5 | Whole building model calibration and validation through monthly electricity bills; |
| |
6 | Data analysis and elaboration of the optimization strategy through post-occupancy experimental assessment; |
| |
7 | Dynamic simulation of the optimized scenatio (Scenario 1): year-round simulation of the case study model after the implementation of the proposed strategy; |
| |
8 | Analysis of results. |
|
2.2. Choice of the Building
2.3. Building Characterization
Architectural Element | AutoCad Architectural Element (Out:left-In:right) | Layers Materials description and thickness (from other side) | Thermal proprieties Transmittance U; Thermal capacity Ct |
---|---|---|---|
External Wall Basements | |
| U = 0.27 W/m2 K; Ct = 14.9 kJ/m2 K |
External Wall from Ground Floor to 5th floor | |
| U = 0.44 W/m2 K Ct = 168.3 kJ/m2 K |
External Wall 6th Floor to 14th floor | |
| U = 0.41 W/m2 K Ct = 18.5 kJ/m2 K |
Internal Partitions from B3 floor to 14th floor | |
| U = 0.34 W/m2 K Ct = 33.9 kJ/m2 K |
Ground Floor | |
| U = 0.16 W/m2 K Ct = 178.4 kJ/m2 K |
Internal Ceiling | |
| U = 1.68 W/m2 K Ct = 191.1 kJ/m2 K |
Roof | |
| U = 2.12 W/m2 K Ct = 230.2 kJ/m2 K |
2.4. Building Modeling and Energy Simulation of the Case Study
- i.
- Preliminary assessment of design documents: drawings and reports about architectural, mechanical, electrical, and functional systems;
- ii.
- Description of building geometry layout within the physical modeling interface;
- iii.
- Elaboration of the energy model through the characterization of the building architectural elements (external walls, ceilings, roof, internal partitions, doors and windows, etc.) and their thermal properties (Table 2);
- iv.
- Description of the building thermal equipment and utility supplies within the energy model, characterizing each thermal zone with its equipment for the final analysis of consumption;
- v.
- Assessment of the control systems and characterization of the actual schedules, to realistically represent the base case scenario, that is a pre-occupancy based scenario;
- vi.
- Elaboration of the base case scenario (Scenario 0) consisting of the continuous operation of the overall energy equipment, to maintain temperature and CO2 levels under the limit values all over the year;
- vii.
- Energy simulation of the base case scenario;
- viii.
- In-field post-occupancy analyses consisting of: temperature and CO2 levels monitoring, occupants’ participation to surveys and interviews;
- ix.
- Elaboration of the optimized scenario model (Scenario 1), thanks to the information collected in phase viii;
- x.
- xi.
- Simulation of the year-round performance of the optimized scenario;
- xii.
- Analysis of the results: evaluation of the potential benefits of such post-occupancy based strategy.
2.5. Post Occupancy Evaluation through in-Situ Analysis
- (i)
- the overall perception about thermal-acoustic-lighting characteristics within the most visited indoor spaces such as classrooms, cafeteria, fitness areas, atrium, swimming-pool area, 8th floor lounge area;
- (ii)
- possible sources of dissatisfaction and the relative causes;
- (iii)
- location of the most used indoor thermal zones for spending free time and main activities that the students used to do in that time;
- (iv)
- principal activities and sport facilities used by each participant.
First Name and Family name (optional) | ||||||||||
M | F | What is your age? | 17–21 | 21–30 | More than 30 | |||||
Do you live in the City? | YES | NO | Where do you come from (US or extra-US)? | |||||||
Too cold | Too hot | Too noisy | Too dark | Comfortable | I do not know | |||||
About classrooms | Too cold | Too hot | Too noisy | Too dark | Comfortable | I do not know | ||||
Too cold | Too hot | Too noisy | Too dark | Comfortable | I do not know | |||||
Too cold | Too hot | Too noisy | Too dark | Comfortable | I do not know | |||||
Too cold | Too hot | Too noisy | Too dark | Comfortable | I do not know | |||||
Too cold | Too hot | Too noisy | Too dark | Comfortable | I do not know | |||||
Too cold | Too hot | Too noisy | Too dark | Comfortable | I do not know | |||||
Too cold | Too hot | Too noisy | Too dark | Comfortable | I do not know | |||||
Studying | Teaching | Meeting people | Administration | Technical support | Other (…) | |||||
If you are a Student, approximately, how much time do you spend in classrooms every day? | 1–2 h/day | 3–4 h/day | 5–6 h/day | >6 h/day | ||||||
YES | NO | |||||||||
Fitness | Racketball | Swimming | Volleyball | Basket | Other (…) | |||||
Approximately how much time do you spend playing sports at VC every week (in hours)? | 1–3 | 4–7 | 7–14 | More than 14 | ||||||
Corridors | Classrooms | Lounge areas | Cafeteria | Fitness centre | Abroad | |||||
Do you usually spend your free-time alone or with friends at VT? | Alone | In small groups (<3 people) | In large groups (>3 people) | |||||||
What is your role at VC? | Student | Professor | Technician | Visitor | Other (…) | |||||
What is the most crowded place at VT? | Corridors | Classrooms | Lounge areas | Cafeteria | Fitness centre | Nowhere | ||||
How satisfied are you with those crowded places (if there are)? | Very Satisfied | Somewhat Satisfied | Undecided | Somewhat Dissatisfied | Very Dissatisfied | |||||
Is there anything that you would like to change, to improve VC liveability? | YES | NO | ||||||||
If YES, what and why would you like to improve? | ||||||||||
Thank you your time! |
Thermal Zone | Floor Positioning | CO2 Levels (ppm) | Air Temperature (°C) | Relative Humidity (%) |
---|---|---|---|---|
Pool | Basement 3rd | 1007 | 24.5 | 55.8 |
Theatre | Basement 3rd | 792 | 22.5 | 14.5 |
Recital Hall | Basement 3rd | 887 | 21.8 | 21.4 |
Gym | Basement 3rd | 928 | 21 | 22.5 |
Auxiliary Gym | Basement 3rd | 1036 | 29 | 25.3 |
North Atrium | Ground floor | 891 | 20.5 | 16.8 |
Court Atrium | Ground floor | 865 | 20.6 | 18.3 |
South Atrium | Ground floor | 1044 | 17 | 14.9 |
Cafeteria | Ground floor | 922 | 20.6 | 17.3 |
Court Servery | Ground floor | 784 | 21.6 | 9.1 |
Kitchen | Ground floor | 806 | 21.6 | 12.8 |
Court Lounge | 8th floor | 881 | 23 | 16.8 |
Court Lounge 2 | 8th floor | 880 | 22.9 | 16.8 |
Office 215 | 8th floor | 850 | 22.9 | 16.1 |
Laboratory 160 | 8th floor | 839 | 22 | 15.6 |
Lecture Room | 14th floor | 959 | 20.6 | 21.7 |
Multipurpose Room | 14th floor | 815 | 20.7 | 15.5 |
3. Energy Optimization Strategy
4. Whole Building Model Calibration and Validation Procedure
5. Discussion of Results
5.1. Potential Energy Saving for each Floor
- -
- from the 4th to the 13th floor: thanks to the real use analysis of classrooms (east side) and offices (west side), the predicted energy saving is up to 38% in January for the 13th floor (32% at 8th floor, 28% at 11th floor, and 27% at 6th floor);
- -
- ground floor and basement 2 have almost negligible energy savings, given the few rescheduling operations allowed;
- -
- basement 1 and basement 3 have very large energy saving potential due to the wrong facilities’ operation of sport clubs and theatre areas. The predicted savings are up to 16% and 10% in January for basement 3 and 1, respectively.
5.2. Whole Building Assessment
6. Conclusions and Future Development of the Research
Acknowledgments
References
- Chwieduk, D. Towards sustainable-energy buildings. Appl. Energy 2003, 76, 211–217. [Google Scholar] [CrossRef]
- Intergovernmental Panel on Climate Change. Climate Change 2007: Synthesis Report; IPCC Fourth Assessment Report. IPCC: Geneva, Switzerland, 2007. No. 104. Available online: http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_synthesis_report.htm (accessed on 12 November 2012).
- U.S. Green Building Council. Available online: http://www.usgbc.org/Default.aspx (accessed on 12 June 2012).
- Chen, S.; Huang, J. A Smart green building: An environmental health control design. Energies 2012, 5, 1648–1663. [Google Scholar] [CrossRef]
- Domínguez, S.; Sendra, J.J.; León, A.L. Towards energy demand reduction in social housing. Energies 2012, 5, 2263–2287. [Google Scholar] [CrossRef]
- Wei, G.; Turner, W.D.; Claridge, D.; Liu, M. Single-Duct Constant Air Volume System Supply Air Temperature Reset: Using Return Air Temperature or Outside Air Temperature? Proceedings of the Second International Conference for Enhanced Building Operations, Richardson, TX, USA, 14–18 October 2002; Available online: http://txspace.di.tamu.edu/bitstream/handle/1969.1/5165/ESL-IC-02-10-01.pdf (accessed on 9 July 2012).
- Cotana, F.; Rossi, F.; Nicolini, A. Evaluation and optimization of an innovative low-cost photovoltaic solar concentrator. Int. J. Photoenergy 2011, 1, 1–10. [Google Scholar] [CrossRef]
- Asdrubali, F.; Cotana, F.; Messineo, A. On the evaluation of solar greenhouse efficiency in building simulation during the heating period. Energies 2012, 5, 1864–1880. [Google Scholar] [CrossRef]
- Peschiera, G.; Taylor, J.E.; Siegel, J.A. Response-relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data. Energy Build. 2010, 42, 1329–1336. [Google Scholar] [CrossRef]
- Pisello, A.L.; Taylor, J.E.; Xu, X.; Cotana, F. Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions. Build. Environ. 2012, 58, 37–45. [Google Scholar] [CrossRef]
- Xu, X.; Taylor, J.E.; Pisello, A.L.; Culligan, P. The impact of place-based affiliation networks on energy conservation: An Holistic model that integrates the influence of buildings, residents and the neighborhood context. Energy Build. 2012, 55, 637–646. [Google Scholar] [CrossRef]
- Bo-ot, L.M.; Wang, Y.H.; Chiang, C.M.; Lai, C.M. Effects of a green space layout on the outdoor thermal environment at the neighborhood level. Energies 2012, 5, 3723–3735. [Google Scholar] [CrossRef]
- Wang, Z.; Wang, L.; Dounis, A.I.; Yang, R. Multi-agent control system with information fusion based comfort model for smart buildings. Appl. Energy 2012, 99, 247–254. [Google Scholar] [CrossRef]
- Ouyang, J.; Ge, J.; Hokao, K. Economic analysis of energy-saving renovation measures for urban existing residential buildings in China based on thermal simulation and site investigation. Energy Policy 2009, 37, 140–149. [Google Scholar] [CrossRef]
- Popescu, D.; Bienert, S.; Schützenhofer, C.; Boazu, R. Impact of energy efficiency measures on the economic value of buildings. Appl. Energy 2012, 89, 454–463. [Google Scholar] [CrossRef]
- Yu, Z.; Haghighat, F.; Fung, B.C.M.; Zhou, L. A novel methodology for knowledge discovery through mining associations between building operational data. Energy Build. 2012, 47, 430–440. [Google Scholar] [CrossRef]
- Claridge, D.E.; Liu, M.; Deng, S.; Turner, W.D.; Haberl, J.S.; Lee, S.U.; Abbas, M.; Bruner, H.; Lee, B.V.S.U. Cutting heating and cooling use almost in half without capital expenditure in a previously retrofit building. European Council for an energy efficient economy. In Proceedings of European Council for an Energy Efficient Economy (ECEEE) 2011 Summer Study, Niagara Falls, NY, USA, 26–29 July 2011; Volume 4, pp. 74–85.
- Kolokotsa, D.; Rovas, D.; Kosmatopoulos, E.; Kalaitzakis, K. A roadmap towards intelligent net zero- and positive-energy buildings. Sol. Energy 2011, 85, 3067–3084. [Google Scholar] [CrossRef]
- Alwaer, H.; Clements-Croome, D.J. Key performance indicators (KPIs) and priority setting in using the multi-attribute approach for assessing sustainable intelligent buildings. Build. Environ. 2010, 45, 799–807. [Google Scholar] [CrossRef]
- Bordass, B.; Cohen, R.; Standeven, M.; Leaman, A. Assessing building performance in use 3: Energy performance of the Probe buildings. Build. Res. Inform. 2001, 29, 114–128. [Google Scholar] [CrossRef]
- Menezes, A.C.; Cripps, A.; Bouchlaghem, D.; Buswell, R. Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap. Appl. Energy 2012, 97, 355–364. [Google Scholar] [CrossRef] [Green Version]
- Deuble, M.P.; de Dear, R.J. Green occupants for green buildings: The missing link? Build. Environ. 2012, 56, 21–27. [Google Scholar] [CrossRef]
- Zalejska-Jonsson, A. Evaluation of low-energy and conventional residential buildings from occupants’ perspective. Build. Environ. 2012, 58, 135–144. [Google Scholar] [CrossRef]
- Gill, Z.M.; Tierney, M.J.; Pegg, I.M.; Allan, N. Low-energy dwellings: The contribution of behaviours to actual performance. Build. Res. Inform. 2010, 38, 491–508. [Google Scholar] [CrossRef]
- Balaras, C.A. Energy Retrofit of a Neoclassic Office Building Social Aspects and Lessons Learned. In proceedings of American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) Annual Winter Meeting, Atlanta, GA, USA, 27–31 January 2001; Volume 107-1, pp. 191–197.
- Murakami, Y.; Masaaki, T.; Kana, M.; Masayuki, H.; Satoru, K. Field experiments on energy consumption and thermal comfort in the office environment controlled by occupants’ requirements from PC terminal. Build. Environ. 2007, 42, 4022–4027. [Google Scholar] [CrossRef]
- Tuohy, R.P.; Humphreys, M.A.; Nicol, J.F.; Samuel, A.; Clarke, J. Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy Build. 2007, 39, 823–836. [Google Scholar] [CrossRef]
- Redmann, N.; Mingsheng, L. Improving Building Control and System Operation Through the Continuous Commissioning® Process: A Case Study. Proceedings of the Third International Conference for Enhanced Building Operations, Berkeley, CA, USA, 13–15 October 2003; Architectural Engineering—Faculty Publications; ESL-IC-03-10-13. Available online: http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1036&context=archengfacpub (accessed on 9 July 2012).
- Gou, Z.H.; Lau, S.; Chen, F. Subjective and objective evaluation of the thermal environment in a three-star green office building in China. Indoor Built Environ. 2012, 21, 412–422. [Google Scholar] [CrossRef]
- Dall’O’, G.; Sarto, L.; Sanna, N.; Martucci, A. Comparison between predicted and actual energy performance for summer cooling in high-performance residential buildings in the Lombardy region (Italy). Energy Build. 2012, 54, 234–242. [Google Scholar] [CrossRef]
- Pisello, A.L.; Xu, X.; Taylor, J.E.; Cotana, F. Network of buildings’ impact on indoor thermal performance. Smart Sustain. Built Environ. 2012, 1, 73–86. [Google Scholar] [CrossRef]
- Pisello, A.L.; Taylor, J.E.; Xu, X.; Cotana, F. Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions. Build. Environ. 2012, 58, 37–45. [Google Scholar] [CrossRef]
- Crawley, B.D.; Pedersen, C.O.; Lawrie, L.K.; Winkelmann, F.C. EnergyPlus: Energy simulation program. ASHRAE J. 2000, 42, 49–56. [Google Scholar]
- Weather Data. EnergyPlus. Available online: http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data2.cfm/region=4_north_and_central_america_wmo_region_4 (accessed on 9 July 2012).
- Dall’O’, G.; Speccher, A.; Bruni, E. The green energy audit, a new procedure for the sustainable auditing of existing buildings integrated with the LEED protocols. Sustain. Cities Soc. 2012, 3, 54–65. [Google Scholar] [CrossRef]
- ASHRAE. ASHRAE Guideline 14–2002 for Measurement of Energy and Demand Savings; ASHRAE: Atlanta, GA, USA, 2002. [Google Scholar]
- Efficiency Valuation Organization. International Performance Measurement and Verification Protocol; Efficiency Valuation Organization: Washington, DC, USA, 2007. [Google Scholar]
- Nexant, Inc. M&V Guidelines: Measurement and Verification for Federal Energy Projects, Version 3.0; US Department of Energy: Washington, DC, USA, 2008. [Google Scholar]
- Fink, A. How to Conduct Surveys. A Step-by-Step Guide, 4th ed.; SAGE Publications, Inc.: Thousand Oaks, CA, USA, 2009; pp. 1–136. [Google Scholar]
- Howard, B.; Parshall, L.; Thompson, J.; Hammer, S.; Dickinson, J.; Modi, V. Spatial distribution of urban building energy consumption by end use. Energy Build. 2012, 45, 141–151. [Google Scholar] [CrossRef]
- Pisello, A.L.; Goretti, M.; Cotana, F. A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity. Appl. Energy 2012, 97, 419–429. [Google Scholar] [CrossRef]
- Newsham, G.R.; Mancini, S.; Birt, B.J. Do LEED-certified buildings save energy? Yes, but... Energy Build. 2009, 41, 897–905. [Google Scholar] [CrossRef]
- Raftery, P.; Keane, M.M.; O’Donnell, J. Calibrating whole building energy models: An evidence-based methodology. Energy Build. 2011, 43, 2356–2364. [Google Scholar] [CrossRef]
- Raftery, P.; Keane, M.M.; Costa, A. Calibrating whole building energy models: Detailed case study using hourly measured data. Energy Build. 2011, 43, 3666–3679. [Google Scholar] [CrossRef]
- ASHRAE. ASHRAE Handbook of Fundamentals; ASHRAE: Atlanta, GA, USA, 2005; ISBN 13-978-1931862738. [Google Scholar]
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Pisello, A.L.; Bobker, M.; Cotana, F. A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy. Energies 2012, 5, 5257-5278. https://doi.org/10.3390/en5125257
Pisello AL, Bobker M, Cotana F. A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy. Energies. 2012; 5(12):5257-5278. https://doi.org/10.3390/en5125257
Chicago/Turabian StylePisello, Anna Laura, Michael Bobker, and Franco Cotana. 2012. "A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy" Energies 5, no. 12: 5257-5278. https://doi.org/10.3390/en5125257