Abstract
A deterministic approach to building energy simulation risks the omission of real-world uncertainties leading to prediction errors. This paper highlights limitations of this approach by contrasting it with a probabilistic uncertainty/sensitivity simulation approach. Latin hypercube sampling (LHS) generates 15000 unique model configurations to assess the effects of weather, physical and operational uncertainties on the annual and peak cooling energy demands for a residential building which situated in a hot and dry climatic region. Probabilistic simulations predicted 0.22–2.17 and 0.45–1.62 times variation in annual and peak cooling energy demands, respectively, compared to deterministic simulation. A novel density-based global sensitivity analysis (SA), i.e., PAWN, is adopted to identify dominant input uncertainties. Unlike traditional SA methods, PAWN allows simultaneous treatment of continuous and categorical inputs from a generic input-output sample. PAWN is favourable when computational resources are limited and model outputs are skewed or multi-modal. For annual and peak cooling demands, the effects of weather and operational parameters associated with airconditioner and window operation are much stronger than these of other parameters considered. Consequently, these parameters warrant greater attention during modelling and simulation stages. Bootstrapping and convergence analysis also confirm the validity of these results.
Similar content being viewed by others
Abbreviations
- BEC:
-
building energy code
- BSP:
-
building simlation program
- CDF:
-
cumulative distribution function
- EPI:
-
energy performance index
- GSA:
-
global sensitivity analysis
- LSA:
-
local sensitivity analysis
- PDF:
-
probability distribution function
- SA:
-
sensitivity analysis
- TMY:
-
typical meteorological year
- UA:
-
uncertainty analysis
- VBSA:
-
variance-based sensitivity analysis
References
ASHRAE (2001). ASHRAE Fundamental Handbook. Atlanta: American Society of Heating, Refrigeration and Air-Conditioning Engineers.
Awbi HB, Hatton A (2000). Natural convection from heated room surfaces. Energy and Buildings, 32: 153–166.
BEE (2018a). Eco-Niwas Samhita 2018 (Energy Conservation Building Code for Residential Buildings), India: Bureau of Energy Efficiency (BEE). Available at https://www.beeindia.gov.in/sites/default/files/ECBC_BOOK_Web.pdf
BEE (2008b). Residential building energy labeling program. India: Bureau of Energy Efficiency (BEE). Available at https://beeindia.gov.in/sites/default/files/LabellingFlyer.pdf
Belazi W, Ouldboukhitine SE, Chateauneuf A, et al. (2018). Uncertainty analysis of occupant behavior and building envelope materials in office building performance simulation. Journal of Building Engineering, 19: 434–448.
BIS (1985). IS 2185 (Part 3)-1984. Specification for concrete masonry units, Part 3: Autoclaved cellular aerated concrete blocks. India: Bureau of Indian Standard (BIS). Available at https://law.resource.org/pub/in/bis/S03/is.2185.3.1984.pdf
BIS (1987). SP: 41 (S&T)-1987. Handbook on functional requirements of buildings. India: Bureau of Indian Standard (BIS). Available at https://law.resource.org/pub/in/bis/S03/is.sp.41.1987.pdf
Chaturvedi S, Rajasekar E, Natarajan S (2020). Multi-objective building design optimization under operational uncertainties using the NSGA II algorithm. Buildings, 10: 88.
Clarke JA, Yaneske PP (2009). A rational approach to the harmonisation of the thermal properties of building materials. Building and Environment, 44: 2046–2055.
Cox S (2016). Building energy codes: policy overview and good practices. Office of Scientific and Technical Information (OSTI), USA.
Crawley DB (1998). Which weather data should you use for energy simulations of commercial buildings? ASHRAE Transactions, 104: 498–515.
Crawley DB, Lawrie LK, Winkelmann FC, et al. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.
Daniel L, Soebarto V, Williamson T (2015). House energy rating schemes and low energy dwellings: The impact of occupant behaviours in Australia. Energy and Buildings, 88: 34–44.
de Wit S, Augenbroe G (2002). Analysis of uncertainty in building design evaluations and its implications. Energy and Buildings, 34: 951–958.
Domínguez-Muñoz F, Anderson B, Cejudo-López JM, et al. (2010a). Uncertainty in the thermal conductivity of insulation materials. Energy and Buildings, 42: 2159–2168.
Domínguez-Muñoz F, Cejudo-López JM, Carrillo-Andrés A (2010b). Uncertainty in peak cooling load calculations. Energy and Buildings, 42: 1010–1018.
Eisenhower B, O’Neill Z, Fonoberov VA, et al. (2012). Uncertainty and sensitivity decomposition of building energy models. Journal of Building Performance Simulation, 5: 171–184.
Evans M, Roshchanka V, Graham P (2017). An international survey of building energy codes and their implementation. Journal of Cleaner Production, 158: 382–389.
Gagnon R, Gosselin L, Decker S (2018). Sensitivity analysis of energy performance and thermal comfort throughout building design process. Energy and Buildings, 164: 278–294.
Gaterell MR, McEvoy ME (2005). The impact of climate change uncertainties on the performance of energy efficiency measures applied to dwellings. Energy and Buildings, 37: 982–995.
Ghanem R, Higdon D, Owhadi H (2017). Handbook of Uncertainty Quantification. New York: Springer.
Goffart J, Mara T, Wurtz E (2017). Generation of stochastic weather data for uncertainty and sensitivity analysis of a low-energy building. Journal of Building Physics, 41: 41–57.
Guo R, Hu Y, Liu M, et al. (2019). Influence of design parameters on the night ventilation performance in office buildings based on sensitivity analysis. Sustainable Cities and Society, 50: 101661.
Heiselberg P, Brohus H, Hesselholt A, et al. (2009). Application of sensitivity analysis in design of sustainable buildings. Renewable Energy, 34: 2030–2036.
Hopfe CJ, Hensen JLM (2011). Uncertainty analysis in building performance simulation for design support. Energy and Buildings, 43: 2798–2805.
Hosseini M, Lee B, Vakilinia S (2017). Energy performance of cool roofs under the impact of actual weather data. Energy and Buildings, 145: 284–292.
IEA (2016). Final report annex 53. Total energy use in buildings Analysis and evaluation methods. International Energy Agency (IEA), USA.
Khorashadi Zadeh F, Nossent J, Sarrazin F, et al. (2017). Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model. Environmental Modelling & Software, 91: 210–222.
Kristensen MH, Petersen S (2016). Choosing the appropriate sensitivity analysis method for building energy model-based investigations. Energy and Buildings, 130: 166–176.
Macdonald IA (2002). Quantifying the effects of uncertainty in building simulation. PhD Thesis, University of Strathclyde, UK. Available at http://www.esru.strath.ac.uk/Documents/PhD/macdonald_thesis.pdf
Maltais LG, Gosselin L (2017). Daylighting ‘energy and comfort’ performance in office buildings: Sensitivity analysis, metamodel and Pareto front. Journal of Building Engineering, 14: 61–72.
Menberg K, Heo Y, Choudhary R (2016). Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information. Energy and Buildings, 133: 433–445.
Naspi F, Arnesano M, Stazi F, et al. (2018). Measuring occupants’ behaviour for buildings’ dynamic cosimulation. Journal of Sensors, 2018: 1–17.
Nguyen AT, Reiter S (2015). A performance comparison of sensitivity analysis methods for building energy models. Building Simulation, 8: 651–664.
Pang Z, O’Neill Z (2018). Uncertainty quantification and sensitivity analysis of the domestic hot water usage in hotels. Applied Energy, 232: 424–442.
Pang Z, O’Neill Z, Li Y, et al. (2020). The role of sensitivity analysis in the building performance analysis: A critical review. Energy and Buildings, 209: 109659.
Pérez-Lombard L, Ortiz J, González R, et al. (2009). A review of benchmarking, rating and labelling concepts within the framework of building energy certification schemes. Energy and Buildings, 41: 272–278.
Pianosi F, Wagener T (2015). A simple and efficient method for global sensitivity analysis based on cumulative distribution functions. Environmental Modelling & Software, 67: 1–11.
Pianosi F, Iwema J, Rosolem R, et al. (2017). A multimethod global sensitivity analysis approach to support the calibration and evaluation of land surface models. In: Sensitivity Analysis in Earth Observation Modelling (Petropoulos GP, Srivastava PK Eds). Netherlands: Elsevier.
Pianosi F, Wagener T (2018). Distribution-based sensitivity analysis from a generic input-output sample. Environmental Modelling & Software, 108: 197–207.
Pinteric M (2017). Building Physics—From physical principles to international standards. Switzerland: Springer.
Prada A, Pernigotto G, Baggio P, et al. (2018). Uncertainty propagation of material properties in energy simulation of existing residential buildings: The role of buildings features. Building Simulation, 11: 449–464.
Pusat S, Ekmekçi I, Akkoyunlu MT (2015). Generation of typical meteorological year for different climates of Turkey. Renewable Energy, 75: 144–151.
Rijal HB, Tuohy P, Humphreys MA, et al. (2007). Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings, 39: 823–836.
Saltelli A, Tarantola S, Campolongo F, et al. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. New York: Wiley.
Sarrazin F, Pianosi F, Wagener T (2016). Global Sensitivity Analysis of environmental models: Convergence and validation. Environmental Modelling & Software, 79: 135–152.
Silva AS, Ghisi E (2014). Uncertainty analysis of user behaviour and physical parameters in residential building performance simulation. Energy and Buildings, 76: 381–391.
Singh R, Lazarus IJ, Kishore VVN (2016). Uncertainty and sensitivity analyses of energy and visual performances of office building with external Venetian blind shading in hot-dry climate. Applied Energy, 184: 155–170.
Struck C, Hensen J, Kotek P (2009). On the application of uncertainty and sensitivity analysis with abstract building performance simulation tools. Journal of Building Physics, 33: 5–27.
Sun Y, Gu L, Wu CFJ, et al. (2014). Exploring HVAC system sizing under uncertainty. Energy and Buildings, 81: 243–252.
Sun Y (2015). Sensitivity analysis of macro-parameters in the system design of net zero energy building. Energy and Buildings, 86: 464–477.
ThermoWorks (2020). Infrared emissivity table. Available at https://www.thermoworks.com/emissivity-table
Tian W (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews, 20: 411–419.
Tian W, Song J, Li Z, et al. (2014). Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis. Applied Energy, 135: 320–328.
Tian W, Liu Y, Zuo J, et al. (2017). Building energy assessment based on a sequential sensitivity analysis approach. Procedia Engineering, 205: 1042–1048.
Tian W, Heo Y, de Wilde P, et al. (2018). A review of uncertainty analysis in building energy assessment. Renewable and Sustainable Energy Reviews, 93: 285–301.
Ullah A, Haydarov K, Ul Haq I, et al. (2020). Deep learning assisted buildings energy consumption profiling using smart meter data. Sensors, 20: 873.
Upreti D, Pignatti S, Pascucci S, et al. (2020). A comparison of moment-independent and variance-based global sensitivity analysis approaches for wheat yield estimation with the aquacrop-OS model. Agronomy, 10: 607.
Yao J (2018). Modelling and simulating occupant behaviour on air conditioning in residential buildings. Energy and Buildings, 175: 1–10.
Yassaghi H, Hoque S (2019). An overview of climate change and building energy: Performance, responses and uncertainties. Buildings, 9: 166.
Yu S, Eom J, Evans M, et al. (2014). A long-term, integrated impact assessment of alternative building energy code scenarios in China. Energy Policy, 67: 626–639.
Yu S, Tan Q, Evans M, et al. (2017). Improving building energy efficiency in India: State-level analysis of building energy efficiency policies. Energy Policy, 110: 331–341.
Zeferina V, Birch C, Edwards R, et al. (2019). Sensitivity analysis of peak and annual space cooling load at simplified office dynamic building model. E3S Web of Conferences, 111: 04038.
Zhang Y, Simulation E, Korolija I (2016). Performing complex parametric simulations with jEPlus. In: Proceedings of the 9th International Conference on Sustainable Energy Technologies, Shanghai, China.
Acknowledgements
The authors would like to acknowledge the funding received from the Department of Science and Technology, Government of India (DST/TMD/UKBEE/2017/17). Projects: Zero Peak Energy Demand for India (ZED-I) and Engineering and Physics Research Council EPSRC (EP/R008612/1).
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Chaturvedi, S., Rajasekar, E. Application of a probabilistic LHS-PAWN approach to assess building cooling energy demand uncertainties. Build. Simul. 15, 373–387 (2022). https://doi.org/10.1007/s12273-021-0815-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12273-021-0815-6