Evaluation of the dynamic energy performance gap of green buildings: Case studies in China
Tóm tắt
The green building concept originated from the need and desire for more energy efficient and environmentally friendly construction practices. With a boom in certified green buildings in recent decades, however, various stakeholders have raised concerns about the actual energy performance of such buildings. While studies have shown significant gaps between the expected and actual energy use of green buildings, the methods used in the analyses have been considered inappropriate. A dynamic approach has been suggested to quantify the discrepancy between the expected and actual energy use in green buildings. However, although the concept of the dynamic approach has been discussed in several studies, a process and methods for applying the approach in real applications are not available in the literature. This study introduces a process and methods for practicing the dynamic approach and provides five case studies of using this approach to assess the energy performance gap of green buildings. The analyses show that the dynamic performance gap of the five buildings ranges from 3.0% to 53.5%, with a median of 24.7%, and the average dynamic gap of the HVAC system is nearly two orders of magnitude greater than that of the non-HVAC system. The degraded controls of HVAC systems may be a main cause of the performance gap.
Tài liệu tham khảo
Accame F, Cesare J, Chen Y, Walsh E, Wu Q (2012). Green buildings in the US and China: Bridging the Energy Performance Gap. US Green Building.
Borgstein EH, Lamberts R, Hensen JLM (2016). Evaluating energy performance in non-domestic buildings: A review. Energy and Buildings, 128: 734–755.
Building Energy Research Centre of Tsinghua University, (2019). Annual report on china building energy efficiency 2019. Beijing: China Architecture & Building Press. (in Chinese)
Burman E (2016). Assessing the operational performance of educational buildings against design expectations—A case study approach. PhD Thesis, University College London, UK.
Chen J, Zhao P, Wang X (2011). The research on sino-US green building rating system. Energy Procedia, 5: 1205–1209.
Crawley DB, Lawrie LK, Winkelmann FC, Buhl WF, Huang Y, et al. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.
Diamond R, Opitz M, Hicks T, Von Neida B, Herrera S (2017). Evaluating the energy performance of the first generation of LEED-certified commercial buildings. Office of Scientific & Technical Information Technical Reports, 11: 1–14.
Figueiredo A, Kämpf J, Vicente R, Oliveira R, Silva T (2018). Comparison between monitored and simulated data using evolutionary algorithms: Reducing the performance gap in dynamic building simulation. Journal of Building Engineering, 17: 96–106.
Heo Y, Choudhary R, Augenbroe GA (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings, 47: 550–560.
Jeong W, Kim JB, Clayton MJ, Haberl JS, Yan W (2014). Translating building information modeling to building energy modeling using model view definition. The Scientific World Journal, 2014: 1–21.
Jradi M, Arendt K, Sangogboye FC, Mattera CG, Markoska E, et al. (2018). ObepME: an online building energy performance monitoring and evaluation tool to reduce energy performance gaps. Energy and Buildings, 166: 196–209.
Kampelis N, Gobakis K, Vagias V, Kolokotsa D, Standardi L, et al. (2017). Evaluation of the performance gap in industrial, residential & tertiary near-Zero energy buildings. Energy and Buildings, 148: 58–73.
Lin B, Liu Y, Wang Z, Pei Z, Davies M (2016). Measured energy use and indoor environment quality in green office buildings in China. Energy and Buildings, 129: 9–18.
MOHURD (2005). Design Standard for Energy Efficiency of Public Buildings. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2014). Assessment Standard for Green Building. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2016). Code for Thermal Design of Civil Building. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
NCDC (2018). NOAA hourly observational climate data. Available at https://www.ncdc.noaa.gov/. Accessed 20 Dec 2018.
Newsham GR, Mancini S, Birt BJ (2009). Do LEED-certified buildings save energy? Yes, but.… Energy and Buildings, 41: 897–905.
Norford LK, Socolow RH, Hsieh ES, Spadaro GV (1994). Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: insights from a reconciliation based on the DOE-2 model. Energy and Buildings, 21: 121–131.
O’Neill Z, Pang X, Shashanka M, Haves P, Bailey T (2014). Modelbased real-time whole building energy performance monitoring and diagnostics. Journal of Building Performance Simulation, 7: 83–99.
Pang X, Wetter M, Bhattacharya P, Haves P (2012). A framework for simulation-based real-time whole building performance assessment. Building and Environment, 54: 100–108.
Petersen S, Hviid C (2012). The European Energy Performance of Buildings Directive: Comparison of calculated and actual energy use in a Danish office building. In: Proceedings of IBPSA England 1st Building Simulation and Optimisation Conference.
Raftery P, Keane M, O’Donnell J (2011). Calibrating whole building energy models: An evidence-based methodology. Energy and Buildings, 43: 2356–2364.
Salehi M, Cavka B T, Fedoruk L, Frisque A, Whitehead D, Bushe W (2013). Improving the performance of a whole-building energy modeling tool by using post-occupancy measured data. In: Proceedings of the 13th International IBPSA Building Simulation Conference.
Samuelson HW, Ghorayshi A, Reinhart CF (2016). Post-occupancy evaluation and partial-calibration of 18 design-phase energy models. In: Proceedings of ASHRAE/IBPSA USA Building Simulation Conference.
Scofield JH (2009). Do LEED-certified buildings save energy? Not really.…Energy and Buildings, 41: 1386–1390.
Teng J, Zhang W, Wu X, Zhang L (2016). Overcoming the barriers for the development of green building certification in China. Journal of Housing and the Built Environment, 31: 69–92.
Turner C, Frankel M (2008). Energy performance of LEED® for new construction buildings. New Buildings Institute, : 1–46.
USGBC (2018). Available at www.usgbc.org/articles/usgbc-statistics. Accessed 15 Jun 2018.
Wang L, Greenberg S, Fiegel J, Rubalcava A, Earni S, et al. (2013). Monitoring-based HVAC commissioning of an existing office building for energy efficiency. Applied Energy, 102: 1382–1390.
Wu X, Peng B, Lin B (2017). A dynamic life cycle carbon emission assessment on green and non-green buildings in China. Energy and Buildings, 149: 272–281.
Xin Y, Lu S, Zhu N, Wu W (2012). Energy consumption quota of four and five star luxury hotel buildings in Hainan Province, China. Energy and Buildings, 45: 250–256.
Yue TX, Wang YA, Liu YJ, Chen PS, Qiu DS, et al. (2005). Surface modelling of human population distribution in China. Ecological Modelling, 181: 461–478.
Zaid SM, Kiani A (2016). Energy prediction versus energy performance of green buildings in Malaysia. Comparison of predicted and operational measurement of GBI certified green office in Kuala Lumpur. MATEC Web of Conferences, 66: 00071.
Zhang Q (2004). Separation of horizontal solar radiation into direct and diffuse components with gompertz funciton. Journal of Environmental Engineering (Transactions of AIJ), 69: 31–37.
Zhang Q (2006). Development of the typical meteorological database for Chinese locations. Energy and Buildings, 38: 1320–1326.
Zhang Q, Huang J (2002). Development of typical year weather data for Chinese locations. ASHRAE Transactions, 108(2): 1063–1075.
Zhang Q, Lou C, Yang H (2004). A new method to separate horizontal solar radiation into direct and diffuse components. In: Proceedings of the ISES Asia-Pacific.
Zou Y, Zhao W, Zhong R (2017). The spatial distribution of green buildings in China: Regional imbalance, economic fundamentals, and policy incentives. Applied Geography, 88: 38–47.