Abstract
The dynamic integration of building retrofit investment tools and linear power systems optimisation tools requires the development of simplified linear building energy models which are representative of different Energy Conservation Measures (ECMs) options. Ensemble Calibration is a methodology which identifies linear building energy models as functions of ECMs for opaque building envelope components. The methodology uses Particle Swarm Optimisation (PSO), a heuristic optimisation algorithm, to minimise the calibration error between model predictions and suitable baseline data. The Ensemble Calibration methodology cannot model fast building thermal response characteristics, such as glazing parameters (e.g., thermal transmittance and solar transmittance) or air leakage parameters (e.g., infiltration rate), as functions of transparent envelope ECMs. The standard PSO algorithm widely explores the solution space while attracting all particles (i.e., candidate model solutions) to the best solution at each iteration. Fast building response parameters are significantly altered during the early iterations of the PSO algorithm, thus having a negative impact on the overall calibration process. Therefore, the glazing and infiltration parameters are not correctly identified in an Ensemble Calibration framework and calibration accuracy of the building models suffers as a result. The current paper addresses this issue through the augmentation of existing Ensemble models using supplementary retrofit parameter functions for non-opaque ECMs. The paper also proposes a simplified infiltration model which emulates improvements in air tightness associated with the addition of ECMs while enabling other air tightness measures to be included as ECMs. The proposed methodology is applied to the Ensemble Calibration of two EnergyPlus archetype models representative of the detached housing stock and mid-floor apartment stock in Ireland. The augmentation algorithm results in the accurate calibration of linear building energy models for different ECM configurations (i.e., ECM combination options), while providing considerable computational advantages. The proposed methodology enables the use of glazing and infiltration scenarios in an Ensemble Calibration framework, thus enhancing the representativeness of the methodology for the integrated analysis of ECM investment planning under future electrified space heating scenarios.
Similar content being viewed by others
References
AECOM (2013). Cost Optimal Calculations and Gap Analysis for Recast EPBD for Residential Buildings. Dublin, Ireland: Department of the Environment, Community and Local Government.
Alley RB, Hewitson B, Hoskins BJ, Joos F, Jouzel J, Kattsov V, Lohmann U (2016). Climate Action Now-Summary for Policymakers. Bonn, Germany: United Nations Climate Change Secretariat.
Andrade-Cabrera C, Turner WJN, Burke D, Neu O, Finn DP (2016). Lumped parameter building model calibration using particle swarm optimization. In: Proceedings of the 3rd Asia Conference of International Building Performance Simulation Association (ASIM 2016), Jeju, Korea.
Andrade-Cabrera C, Burke D, Turner WJN, Finn DP (2017). Ensemble Calibration of Lumped Parameter Retrofit Models Using Particle Swarm Optimization. Energy And Buildings, 155: 513–532.
ASHRAE (2012). International Weather for Energy Calculations 2.0 (IWEC2 Weather Files) Users Manual and CD-ROM. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.
ASHRAE (2017). 2017 ASHRAE Handbook-Fundamentals. Atlanta, USA: American Society of Heating, Refrigerating and Air- Conditioning Engineers.
Ault G, Clarke J, Gill S, Hand J, Kim J, Kockar I, Svehla K (2013). The use of simulation to optimise scheduling of domestic electric storage heating within smart grids. In: Proceedings of the 2nd IBPSA-England Conference (BSO2014), London, UK.
Bakirtzis GA, Biskas PN, Chatziathanasiou V (2012). Generation expansion planning by milp considering mid-term scheduling decisions. Electric Power Systems Research, 86: 98–112.
Cerezo C, Sokol J, AlKhaled S, Reinhart C, Al-Mumin A, Hajiah A (2017). Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City. Energy and Buildings, 154: 321–334.
Crabb JA, Murdoch N, Penman JM (1987). A simplified thermal response model. Building Services Engineering Research and Technology, 8: 13–19.
Dickerhoff DJ, Grimsrud DT, and Lipschutz RD (1982). Component Leakage Testing in Residential Buildings. Report No. LBL-14735. University of California, USA.
EEA (2015). The European Environment: State and Outlook: 2015-Executive Summary. Copenhagen, Denmark: European Energy Agency.
ECF (2010). Roadmap 2050: A Practical Guide to a Prosperous, Low-Carbon Europe. The Hague, Netherlands: European Climate Foundation.
ECF (2011). Power Perspectives 2030: On the Road to a Decarbonised Power Sector. The Hague, Netherlands: European Climate Foundation.
Fan Y, Xia X (2018). Energy-efficiency building retrofit planning for green building compliance. Building and Environment, 136: 312–321.
Fux SF, Ashouri A, Benz MJ, Guzzella L (2014). EKF based self-adaptive thermal model for a passive house. Energy and Buildings, 68: 811–817.
Gillott MC, Loveday DL, White J, Wood CJ, Chmutina K, Vadodaria K (2016). Improving the airtightness in an existing UK dwelling: The challenges, the measures and their effectiveness. Building and Environment, 95: 227–239.
Greensfelder EM, Henze G, Felsmann C (2011). An investigation of optimal control of passive building thermal storage with real time pricing. Journal of Building Performance Simulation, 4: 91–104.
Hassan R, Cohanim B (2005). A comparison of particle swarm optimization and the genetic algorithm. In: Proceedings of the 1st AIAA Multidisciplinary Design Optimization Specialist Conference, Austin, USA.
He X, Zhang Z, Kusiak A (2014). Performance optimization of hvac systems with computational intelligence algorithms. Energy and Buildings, 81: 371–380.
Hong S, Oreszczyna T, Ridley I (2004). The impact of energy efficient refurbishment on the airtightness in English dwellings. In: Proceedings of the 25th AIVC (Air Infiltration and Ventilation Centre) Conference, Prague, Czech Republic.
IPCC (2014). Climate Change 2014 Synthesis Report: Summary Chapter for Policymakers. Geneva, Switzerland: Intergovernmental Panel on Climate Change.
Jermyn D, Richman R (2016). A process for developing deep energy retrofit strategies for single-family housing typologies: Three Toronto case studies. Energy and Buildings, 116: 522–534.
Kinnane O, Sinnott D, Turner WJN (2016). Evaluation of passive ventilation provision in domestic housing retrofit. Building and Environment, 106: 205–218.
Kusiak A, Xu G (2012). Modeling and optimization of HVAC systems using a dynamic neural network. Energy, 42: 241–250.
Lorenz F, Masy G (1982). Method for evaluation of energy efficiency in buildings subjected to intermitted heating: Solution by finite difference of a model with two time constants. Report No. GM820130-01. University of Liège, Belgium. (in French)
Marino S, Hogue IB, Ray CJ, Kirschner DE (2008). A methodology for performing global uncertainty and sensitivity analysis in systems biology. Journal of Theoretical Biology, 254: 178–196.
Mathworks (2015). Global Optimization Toolbox: User’s Guide. The Mathworks.
Muringathuparambil RJ, Musango JK, Brent AC, Currie P (2017). Developing building typologies to examine energy efficiency in representative low cost buildings in Cape Town townships. Sustainable Cities and Society 33: 1–17.
Neu O, Oxizidis S, Flynn D, Pallonetto F, Finn DP (2014). Developing building archetypes for electrical load shifting assesment: Analysis of Irish residential stock. In: Proceedings of the 4th CIBSE ASHRAE Technical Symposium, Dublin, Ireland.
Neu O (2016). Assessment of the electrical flexibility resource of residential building stocks using archetypes. PhD Dissertaton, University College Dublin, Dublin, Ireland.
Pudjianto D, Aunedi M, Djapic P, Strbac G (2014). Whole-systems assessment of the value of energy storage in low-carbon electricity systems. IEEE Transactions on Smart Grid, 5: 1098–1109.
Reinhart CF, Cerezo C (2016). Urban building energy modeling-A review of a nascent field. Building and Environment, 97: 196–202.
Robertson AF, Gross D (1958). An electrical-analog method for transient heat-flow analysis. Journal of Research of the National Bureau of Standards, 61: 105–115.
SEAI (2012). Dwelling Energy Assessment Procedure (DEAP) version 3.2.1. Dublin, Ireland: The Sustainable Energy Authority of Ireland.
Sinnott D (2016). Dwelling airtightness: A socio-technical evaluation in an Irish context. Building and Environment, 95: 264–271.
Sokol J, Cerezo Davila C, Reinhart CF (2017). Validation of a Bayesian-based method for defining residential archetypes in urban building energy models. Energy and Buildings, 134: 11–24.
Wu R, Mavromatidis G, Orehounig K, Carmeliet J (2017). Multiobjective optimisation of energy systems and building envelope retrofit in a residential community. Applied Energy, 190: 634–649.
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 646116. William Turner is supported by the Science Foundation Ireland Strategic Partnership Programme (SFI/15/SPP/E3125) and the UCD Energy21 program, co-financed through the Marie Sklodowska-Curie program (FP7-PEOPLE-2013-COFUND).
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Andrade-Cabrera, C., Turner, W.J.N. & Finn, D.P. Augmented Ensemble Calibration of lumped-parameter building models. Build. Simul. 12, 207–230 (2019). https://doi.org/10.1007/s12273-018-0473-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12273-018-0473-5