Calibration of building energy simulation models for energy-efficient retrofitting: A residential case study in Samsun-Havza
Öz
Considering today's technological developments, it can be said that academic interest in intelligent systems that provide data about human living spaces has increased significantly. In the energy sector, the growing availability of data from smart evaluation systems and advanced devices, combined with progress in energy modeling software, has notably enhanced the effectiveness of energy modeling and efficiency improvement efforts. Calibration of Building Energy Simulation (BES) models is crucial for ensuring the accuracy required for implementing and evaluating energy efficiency strategies. Organizations such as ASHRAE 14-2014, IPMVP and FEMP are developing model validation methods in this context. This study addresses methodological challenges and reduces uncertainties encountered during the calibration processes of BES models. The primary objective of the research is to contribute to optimizing energy efficiency strategies. Integrating systematic calibration approaches and uncertainty assessment methods is anticipated to enable more accurate energy performance analyses. Methodologically, the study presents an approach to resolve errors in validation measurements within calibration processes. On the empirical side, the applicability of the systematic calibration methodology was successfully tested using forty days of hourly recorded indoor temperature data and indoor temperature data obtained from the EnergyPlus program via DesignBuilder; and validated with N(MBE) and CV(RMSE) uncertainty indices.As a result of the analysis, it was determined that the total final energy consumption (heating, DHW, electricity) of the building in question was 128.31 kWh/m², and approximately 72% of this was heating energy. Calibration results indicated that N(MBE) was 1.68% and CV(RMSE) was 13.86%, both within the thresholds set by ASHRAE 14, FEMP and IPMVP. This result shows that in terms of applicability, the calibrated model can be a practical tool that can be successfully used in energy efficient retrofit proposal development and implementation research.
Anahtar Kelimeler
Building Energy Simulation (BES) Energy-efficient retrofitting Calibration Validation
Destekleyen Kurum
TUBITAK
Proje Numarası
222N354
Teşekkür
This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with project number 222N354. We would like to thank TUBITAK for their support of the project.
Kaynakça
- [1] Li S, Meng J, Zheng H, Zhang N, Huo J, Li Y, Guan D. The driving forces behind the change in energy consumption in developing countries. Environmental Research Letters 2021; 16(5), 054002.
- [2] Tadeu S, Tadeu A, Simões N, Gonçalves M, Prado R. A sensitivity analysis of a cost optimality study on the energy retrofit of a single-family reference building in Portugal. Energy Efficiency 2018; 11:1411-1432.
- [3] World Economic Forum (WEF). (2024). Transforming energy demand 2024. Retrieved from https://www.weforum.org/publications/transforming-energy-demand
- [4] Wang S, Yan C, Xiao F. Quantitative energy performance assessment methods for existing buildings. Energy and buildings 2012; 55: 873-888.
- [5] Lamberts R, Hensen JLM. (Eds.). Building performance simulation for design and operation. Spoon Press, London, UK, 2011.
- [6] Chong A, Gu Y, Jia H. Calibrating building energy simulation models: a review of the basics to guide future work. Energy and Buildings 2021; 253: 111533.
- [7] Ramos Ruiz G, Fernandez Bandera C. (2017). Validation of calibrated energy models: common errors. Energies 2017; 10(10): 1587. https://doi.org/10.3390/en10101587
- [8] Cacabelos A, Eguía P, Febrero L, Granada E. (2017). Development of a new multi-stage building energy model calibration methodology and validation in a public library. Energy and Buildings, 146, 182-199. https://doi.org/https://doi.org/10.1016/j.enbuild.2017.04.071
- [9] Brunelli C, Castellani F, Garinei A, Biondi L, Marconi M. (2016). A procedure to perform multi-objective optimization for sustainable design of buildings. Energies, 9(11), 915. https://www.mdpi.com/1996-1073/9/11/915
- [10] Raftery P, Keane M, O’Donnell J. Calibrating whole building energy models: an evidence-based methodology. Energy Build 2011; 43: 2356–2364.
- [11] Choi W, Joe J, Kwak Y, Huh JH. Operation and control strategies for multi storey double skin facades during the heating season. Energy Build 2012; 49: 454–465.
- [12] Sahin CD, Arsan ZD, Tuncoku SS, Broström T, Akkurt GG. A transdisciplinary approach on the energy efficient retrofitting of a historic building in the Aegean Region of Turkey. Energy Build 2015; 96: 128–139.
- [13] Fabrizio E, Monetti V. Methodologies and advancements in the calibration of building energy models. Energies 2015; 8(4): 2548–2574. https://doi.org/10.3390/en8042548
- [14] Reddy TA, Maor I, Panjapornpon C. Calibrating detailed building energy simulation programs with measured data—part I: General methodology (RP-1051). Hvac&R Research 2007; 13(2): 221–241. https://doi.org/10.1080/10789669.2007.10390951.
- [15] American society of heating, ventilating, and air conditioning Engineers (ASHRAE). Guideline 14-2014: Measurement of energy and demand savings. Atlanta, GA: American Society of Heating, Ventilating, and Air Conditioning Engineers, 2014.
- [16] Webster L, Bradford J, Sartor D, Shonder J, Atkin E, Dunnivant S, Schiller S. M&V guidelines: Measurement and verification for performance-based contracts (Version 4.0). U.S. Department of Energy Federal Energy Management Program, 2015.
- [17] Cowan, J. International performance measurement and verification protocol: Concepts and options for determining energy and water savings - vol. I. International Performance Measurement & Verification Protocol, 2002.
- [18] Coakley D, Raftery P, Keane M. A review of methods to match building energy simulation models to measured data. Renewable and Sustainable Energy Reviews 2014; 37: 123–141.
- [19] Reddy TA, Maor I, Jian S, Panjapornporn C. (2006). Procedures for reconciling computer-calculated results with measured energy data. Ashrae Research Project 2006; 1051: 1-60.
- [20] Robertson J, Polly B, Collis J. Evaluation automated model calibration techniques for residential building energy simulation. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2013.
- [21] Diamond SC, Hunn BD. Comparison of DOE-2 computer program simulations to metered data for seven commercial buildings. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 1981.
- [22] Reddy A. Literature review on calibration of building energy simulation programs: Uses, problems, procedure, uncertainty, and tools. Ashrae Transactions 2006; 112: 226-240.
- [23] Yang Z, Becerik Gerber B. A model calibration framework for simultaneous multi-level building energy simulation. Applied Energy 2015; 149: 415–431.
- [24] Muneer T, Younes S. The all-sky meteorological radiation model: Proposed improvements. Applied Energy 2006; 83: 436–450.
Öz
Proje Numarası
222N354
Kaynakça
- [1] Li S, Meng J, Zheng H, Zhang N, Huo J, Li Y, Guan D. The driving forces behind the change in energy consumption in developing countries. Environmental Research Letters 2021; 16(5), 054002.
- [2] Tadeu S, Tadeu A, Simões N, Gonçalves M, Prado R. A sensitivity analysis of a cost optimality study on the energy retrofit of a single-family reference building in Portugal. Energy Efficiency 2018; 11:1411-1432.
- [3] World Economic Forum (WEF). (2024). Transforming energy demand 2024. Retrieved from https://www.weforum.org/publications/transforming-energy-demand
- [4] Wang S, Yan C, Xiao F. Quantitative energy performance assessment methods for existing buildings. Energy and buildings 2012; 55: 873-888.
- [5] Lamberts R, Hensen JLM. (Eds.). Building performance simulation for design and operation. Spoon Press, London, UK, 2011.
- [6] Chong A, Gu Y, Jia H. Calibrating building energy simulation models: a review of the basics to guide future work. Energy and Buildings 2021; 253: 111533.
- [7] Ramos Ruiz G, Fernandez Bandera C. (2017). Validation of calibrated energy models: common errors. Energies 2017; 10(10): 1587. https://doi.org/10.3390/en10101587
- [8] Cacabelos A, Eguía P, Febrero L, Granada E. (2017). Development of a new multi-stage building energy model calibration methodology and validation in a public library. Energy and Buildings, 146, 182-199. https://doi.org/https://doi.org/10.1016/j.enbuild.2017.04.071
- [9] Brunelli C, Castellani F, Garinei A, Biondi L, Marconi M. (2016). A procedure to perform multi-objective optimization for sustainable design of buildings. Energies, 9(11), 915. https://www.mdpi.com/1996-1073/9/11/915
- [10] Raftery P, Keane M, O’Donnell J. Calibrating whole building energy models: an evidence-based methodology. Energy Build 2011; 43: 2356–2364.
- [11] Choi W, Joe J, Kwak Y, Huh JH. Operation and control strategies for multi storey double skin facades during the heating season. Energy Build 2012; 49: 454–465.
- [12] Sahin CD, Arsan ZD, Tuncoku SS, Broström T, Akkurt GG. A transdisciplinary approach on the energy efficient retrofitting of a historic building in the Aegean Region of Turkey. Energy Build 2015; 96: 128–139.
- [13] Fabrizio E, Monetti V. Methodologies and advancements in the calibration of building energy models. Energies 2015; 8(4): 2548–2574. https://doi.org/10.3390/en8042548
- [14] Reddy TA, Maor I, Panjapornpon C. Calibrating detailed building energy simulation programs with measured data—part I: General methodology (RP-1051). Hvac&R Research 2007; 13(2): 221–241. https://doi.org/10.1080/10789669.2007.10390951.
- [15] American society of heating, ventilating, and air conditioning Engineers (ASHRAE). Guideline 14-2014: Measurement of energy and demand savings. Atlanta, GA: American Society of Heating, Ventilating, and Air Conditioning Engineers, 2014.
- [16] Webster L, Bradford J, Sartor D, Shonder J, Atkin E, Dunnivant S, Schiller S. M&V guidelines: Measurement and verification for performance-based contracts (Version 4.0). U.S. Department of Energy Federal Energy Management Program, 2015.
- [17] Cowan, J. International performance measurement and verification protocol: Concepts and options for determining energy and water savings - vol. I. International Performance Measurement & Verification Protocol, 2002.
- [18] Coakley D, Raftery P, Keane M. A review of methods to match building energy simulation models to measured data. Renewable and Sustainable Energy Reviews 2014; 37: 123–141.
- [19] Reddy TA, Maor I, Jian S, Panjapornporn C. (2006). Procedures for reconciling computer-calculated results with measured energy data. Ashrae Research Project 2006; 1051: 1-60.
- [20] Robertson J, Polly B, Collis J. Evaluation automated model calibration techniques for residential building energy simulation. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2013.
- [21] Diamond SC, Hunn BD. Comparison of DOE-2 computer program simulations to metered data for seven commercial buildings. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 1981.
- [22] Reddy A. Literature review on calibration of building energy simulation programs: Uses, problems, procedure, uncertainty, and tools. Ashrae Transactions 2006; 112: 226-240.
- [23] Yang Z, Becerik Gerber B. A model calibration framework for simultaneous multi-level building energy simulation. Applied Energy 2015; 149: 415–431.
- [24] Muneer T, Younes S. The all-sky meteorological radiation model: Proposed improvements. Applied Energy 2006; 83: 436–450.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Enerji |
| Bölüm | Research Article |
| Yazarlar | |
| Proje Numarası | 222N354 |
| Yayımlanma Tarihi | 26 Haziran 2025 |
| Gönderilme Tarihi | 21 Ocak 2025 |
| Kabul Tarihi | 28 Nisan 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 10 Sayı: 2 |
