Understanding the Reasons behind the Energy Performance Gap of an Energy-Efficient Building, through a Probabilistic Approach and On-Site Measurements
Abstract
:Highlights
- The energy performance gap of a new multi-family Minergie® building was identified by comparing the measured and the calculated heating demand.
- The comparisons between the measured and the calculated heating demand showed that the latter underestimates the real heating demand, by a factor of 2.
- Commonly used hypotheses in the Swiss building practice, considering the climate conditions of the building, the indoor temperature and the electricity consumption, as causes of the increased performance gap, were not justified for this case study.
- A probabilistic framework was then proposed, so as to include the different uncertainties of the parameters of the heating demand, as an attempt to reduce the performance gap.
- A sensitivity analysis showed that the shading factor and the active heat flow were identified as being the most influential on the uncertainty of the heating demand. Correction measures were finally proposed, based on these results.
1. Introduction
2. Materials and Methods
2.1. Building Case Study and Monitoring System, Real and Normative Heating Demand
2.2. Minimization and Explanation of the Performance Gap through Uncertainty and Sensitivity Analyses
3. Results
3.1. Identifying the Energy Performance Gap
3.2. Explaining the Performance Gap by Comparing the Model Assumptions and the Measured Data
3.3. Reducing the Performance Gap, Using a Probabilistic Approach
3.4. Explaining the Performance Gap, Using a Global Sensitivity Analysis
4. Discussion
4.1. Further Reduction of the Performance Gap by Adjusting the Shading Factor
4.2. Limitations of the Current Approach
5. Conclusions
- (a)
- Among the reasons explaining the performance gap, we can find the overestimation of the solar and internal gains in the heating demand calculations, errors of the measurement system, wrong configurations of the technical system, the non-representativeness of the normative values included in the simulations, regarding the real conditions of the building or the uncertainty of the parameters controlled by the occupants.
- (b)
- The probabilistic analysis can be an efficient method for minimizing the performance gap both in ex-post and ex-ante evaluations, while the sensitivity analysis proved to be a straightforward method for the identification of the parameters that explained the variance of the probabilistic heating demand. This step allows a targeted investigation of these parameters, regarding their adjustment in the simulation or in the real construction and gives hints about the causes of the performance gap as well as solutions for its correction.
- (c)
- The results of the sensitivity analysis cannot be generalized since only one residential case study was analyzed. However, the analysis showed that increasing the percentage of the shading factor in the Minergie® simulations for this specific case study can lead to the minimization of the gap between the measurements and the simulation. Further studies are now needed that also includes the use of a more accurate building heating demand model using a dynamic calculation and more measurement data in sensitive parameters (e.g., shading factor).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Parameter | Deterministic Assessment | Probabilistic Assessment | ||
---|---|---|---|---|
Normative Value According to the Minergie® Standard | Range of the Parameter | Comments on the Sampling Type | ||
Parameters (normative values and PDFs) | Shading factor | 0% | [0, 100] % | For each window of the building (total: 43), a random uniform sampling is performed |
Energy reference area per person | 40 m2/person | [30, 50] m2/user | Uniform | |
Internal heat gains per person | 70 W/person | [50, 90] W/user | Uniform | |
Occupant presence per day | 12 h/day | [8, 16] h/day | Uniform | |
Reduction factor for the electricity heating needs | 70% | [70, 100] % | Uniform | |
Active thermal air flow rate 1 | 0.33 m3/(m2ERA∙h) | [0.3, 0.7] m3/(m2ERA∙h) | Uniform | |
Thermal capacity of the envelope | 0.50 MJ/(m2∙K) | [0.45, 0.555] MJ/(m2∙K) | Uniform | |
U-value of the components of the thermal envelope | According to the normative assumptions for the layers of construction materials | ±10% of the calculated U-value in the normative heating demand (Qh) | A U-value is sampled for each building component of the thermal envelope (total: 68 for each simulation) | |
ψ value of the thermal bridges | According to the normative assumptions for the ψ value between building components | ±10% of the calculated ψ value in the normative heating demand (Qh) | A ψ value is sampled for each thermal bridge (total: 7 for each simulation) | |
Mean air flow | 2400 m3/h | - | - | |
Infiltration rate | 0.15 m3/(h. m2ERA) | - | ||
Efficiency of the heat recovery | 80% | - | - | |
Others parameters 1 (normative and measured values) | Electricity consumption (other domestic uses excluding DHW) | 100 MJ/(m2ERA.an) | Average monthly real electricity consumption | - |
External temperature | Normative weather station (Payerne, Switzerland) | Local weather station (Gland, Switzerland) | ||
Solar irradiation | Normative weather station (Payerne, Switzerland) | Local weather station (Gland, Switzerland) | ||
Internal temperature | Monthly average temperatures in each flat of the building taken from the smart meters | Average monthly real internal temperature |
2014 | 2015 | 2016 | 2017 | ||
---|---|---|---|---|---|
Deterministic normative heating demand (Qh) according to the Minergie® label | [kWhuseful] | 44,899 | 44,899 | 44,899 | 44,899 |
Measured heating demand (Qh) | [kWhuseful] | 71,273 | 88,477 | 94,509 | 95,999 |
Relative difference between the deterministic and the measured heating demand | [%] | +59 | +97 | +111 | +114 |
Measurements | Normative Values (SIA 380/1) | |
---|---|---|
External temperature | Climate data of the station in Gland, close to the building. | Default climate data: station of Payerne |
Internal temperature | Mean temperature of the apartments (during the heating season): 21–23 °C | 20 °C |
Electricity consumption | 23 kWh/m2 ERA sd = 1.10 kWh/m2 ERA | 27.8 kWh/m2 ERA |
Energy signature | 1.67 kW/K | 1.55 kW/K |
Measured Parameters Integrated in the Energy Simulation | 2014 | 2015 | 2016 | 2017 | |
---|---|---|---|---|---|
Real climate data | [%] | +227 | +239 | +250 | +213 |
Real climate data + measured indoor temperature | [%] | +158 | +159 | +136 | +146 |
Real climate data + measured indoor temperature + electricity consumption | [%] | +104 | +116 | +96 | +112 |
2014 | 2015 | 2016 | 2017 | |
---|---|---|---|---|
Energy performance gap between measurements and the mean value of the probabilistic Qh [%] | +5% | +18% | +26% | +28% |
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Padey, P.; Goulouti, K.; Wagner, G.; Périsset, B.; Lasvaux, S. Understanding the Reasons behind the Energy Performance Gap of an Energy-Efficient Building, through a Probabilistic Approach and On-Site Measurements. Energies 2021, 14, 6178. https://doi.org/10.3390/en14196178
Padey P, Goulouti K, Wagner G, Périsset B, Lasvaux S. Understanding the Reasons behind the Energy Performance Gap of an Energy-Efficient Building, through a Probabilistic Approach and On-Site Measurements. Energies. 2021; 14(19):6178. https://doi.org/10.3390/en14196178
Chicago/Turabian StylePadey, Pierryves, Kyriaki Goulouti, Guy Wagner, Blaise Périsset, and Sébastien Lasvaux. 2021. "Understanding the Reasons behind the Energy Performance Gap of an Energy-Efficient Building, through a Probabilistic Approach and On-Site Measurements" Energies 14, no. 19: 6178. https://doi.org/10.3390/en14196178