Advertisement

Occupant behavior in identical residential buildings: A case study for occupancy profiles extraction and application to building performance simulation

  • Antonio Muroni
  • Isabella GaetaniEmail author
  • Pieter-Jan Hoes
  • Jan L. M. Hensen
Open Access
Research Article Building Thermal, Lighting, and Acoustics Modeling
  • 106 Downloads

Abstract

This study employs a simplified Knowledge Discovery in Database (KDD) to extract occupancy, equipment and light use profiles from a database referred to 12 all-electric prefabricated dwellings in the Netherlands. The profiles are then integrated into a building performance simulation (BPS) model using the software TRNSYS v17. The significance of the extracted profiles is verified by comparing the total and end-use yearly electricity consumption of the investigated dwellings as predicted by the simulation tool with on-site measurements. For the considered dwellings, using standard OB modeling results in an underestimation of the energy use intensity (EUI) by 5.9% to 42.5%, depending on the case. The integration of the occupant behavior (OB) profiles improves the total electricity consumption prediction from an initial 22.9% average deviation from measurements to 1.7%. The results corroborate that the 1.6x discrepancy observed in the buildings’ energy use intensity could be entirely ascribed to OB. Then, the knowledge extracted from the households’ database is used to propose a local electricity market framework to reduce the electricity bill and grid dependency of all households. This study confirms the need for appropriate OB modeling in BPS, it shows the potential of the KDD method for successful OB profiles extraction, and is a first example of data-mined OB profiles integration in BPS, as well as of OB profiles deployment for a practical application other than energy use prediction.

Keywords

Occupant behavior identical dwellings data-mining occupant behavior profiles 

References

  1. Aerts D, Minnen J, Glorieux I, Wouters I, Descamps F (2013). Discrete occupancy profiles from time-use data for user behaviour modelling in homes. In: Proceedings of the 13th International IBPSA Building Simulation Conference, Chambery, France.Google Scholar
  2. Andersen RK (2012). The influence of occupants’ behaviour on energy consumption investigated in 290 identical dwellings and in 35 apartments. In: Proceedings of the 10th International Conference on Healthy Buildings, Brisbane, Australia.Google Scholar
  3. Attia S, de Herde A, Gratia E, Hensen JLM (2013). Achieving informed decision-making for net zero energy buildings design using building performance simulation tools. Building Simulation, 6: 3–21.CrossRefGoogle Scholar
  4. Bahaj AS, James PAB (2007). Urban energy generation: The added value of photovoltaics in social housing. Renewable and Sustainable Energy Reviews, 11: 2121–2136.CrossRefGoogle Scholar
  5. Basu K, Hawarah L, Arghira N, Joumaa H, Ploix S (2013). A prediction system for home appliance usage. Energy and Buildings, 67: 668–679.CrossRefGoogle Scholar
  6. Boekhoud A, Behrendt L (2013). Taxes and Incentives for Renewable Energy. KPMG International.Google Scholar
  7. Cao S, Hasan A, Sirén K (2013). On-site energy matching indices for buildings with energy conversion, storage and hybrid grid connections. Energy and Buildings, 64: 423–438.CrossRefGoogle Scholar
  8. Cao S, Hasan A, Sirén K (2014). Matching analysis for on-site hybrid renewable energy systems of office buildings with extended indices. Applied Energy, 113: 230–247.CrossRefGoogle Scholar
  9. Clevenger CM, Haymaker J (2006). The Impact of the building occupant on energy modeling simulation. In: Proceedings of Joint International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada.Google Scholar
  10. D’Oca S, Hong T (2014). A data-mining approach to discover patterns of window opening and closing behavior in offices. Building and Environment, 82: 726–739.CrossRefGoogle Scholar
  11. D’Oca S, Hong T (2015). Occupancy schedules learning process through a data mining framework. Energy and Buildings, 88: 395–408.CrossRefGoogle Scholar
  12. D’Oca S, Corgnati S, Hong T (2015). Data mining of occupant behavior in office buildings. Energy Procedia, 78: 585–590.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. Davies DL, Bouldin DW (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2): 224–227.CrossRefGoogle Scholar
  15. Fan C, Xiao F, Yan C (2015). A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Automation in Construction, 50: 81–90.CrossRefGoogle Scholar
  16. Gaetani I, Hoes P-J, Hensen JLM (2016). Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy. Energy and Buildings, 121: 188–204.CrossRefGoogle Scholar
  17. Gram-Hanseen K (2010). Residential heat comfort practices: Understanding users. Building Research & Information, 38: 175–186.CrossRefGoogle Scholar
  18. Guerra Santin O, Itard L, Visscher H (2009). The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential Stock. Energy and Buildings, 41: 1223–1232.CrossRefGoogle Scholar
  19. Hensen JLM (2011). Building performance simulation for sustainable building design and operation. In: Proceedings of the 60th Anniversary Environmental Engineering Department, Prague, Czech Technical University.Google Scholar
  20. Hoes P (2014). Computational performance prediction of the potential of hybrid adaptable thermal storage concepts for lightweight low-energy houses. PhD Thesis, Universiteit Eindhoven, the Netherlands.Google Scholar
  21. Hong T, Lin H-w (2012). Occupant behavior: Impact on energy use of private offices. In: Proceedings of Asim IBSPA Asia Conference.Google Scholar
  22. Hong T, D’Oca S, Taylor-Lange SC, Turner WJN, Chen Y, Corgnati SP (2015). An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema. Building and Environment, 94: 196–205.CrossRefGoogle Scholar
  23. Juodis E, Jaraminiene E, Dudkiewicz E (2009). Inherent variability of heat consumption in residential buildings. Energy and Buildings, 41: 1188–1194.CrossRefGoogle Scholar
  24. Khan A, Nicholson J, Mellor S, Jackson D, Ladha K, Ladha C, Hand J, Clarke J, Olivier P, Plötz T (2014). Occupancy monitoring using environmental & context sensors and a hierarchical analysis framework. In: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings — BuildSys’14.Google Scholar
  25. Maier T, Krzaczek M, Tejchman J (2009). Comparison of physical performances of the ventilation systems in low-energy residential houses. Energy and Buildings, 41: 337–353.CrossRefGoogle Scholar
  26. Monetti V, Davin E, Fabrizio E, André P, Filippi M (2015). Calibration of building energy simulation models based on optimization: A case study. Energy Procedia, 78: 2971–2976.CrossRefGoogle Scholar
  27. Saldanha N, Beausoleil-Morrison I (2012). Measured end-use electric load profiles for 12 Canadian houses at high temporal resolution. Energy and Buildings, 49: 519–530.CrossRefGoogle Scholar
  28. O’Brien W, Gaetani I, Carlucci S, Hoes P-J, Hensen JLM (2017a). On occupant-centric building performance metrics. Building and Environment, 122: 373–385.CrossRefGoogle Scholar
  29. O’Brien W, Gaetani I, Gilani S, Carlucci S, Hoes P-J, Hensen J (2017b). International survey on current occupant modelling approaches in building performance simulation. Journal of Building Performance Simulation, 10: 653–671.CrossRefGoogle Scholar
  30. Pan Y, Huang Z, Wu G (2007). Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai. Energy and Buildings, 39: 651–657.CrossRefGoogle Scholar
  31. Raftery P, Keane M, Costa A (2011). Calibrating whole building energy models: Detailed case study using hourly measured data. Energy and Buildings, 43: 3666–3679.CrossRefGoogle Scholar
  32. RapidMiner (2017). “Rapid Miner Studio.”Google Scholar
  33. Royapoor M, Roskilly T (2015). Building model calibration using energy and environmental data. Energy and Buildings, 94: 109–120.CrossRefGoogle Scholar
  34. Samuelson HW, Ghorayshi A, Reinhart CF (2016). Analysis of a simplified calibration procedure for 18 design-phase building energy models. Journal of Building Performance Simulation, 9: 17–29.CrossRefGoogle Scholar
  35. TRNSYS (2017). TRNSYS V17.Google Scholar
  36. The Engineering Toolbox (2016). Carbon Dioxide Concentration - Comfort Levels.Google Scholar
  37. Urban B, Gomez C (2013). A case for thermostat user models. In: Proceedings of the 13th International IBPSA Building Simulation Conference, Chambery, France.Google Scholar
  38. Yan D, O’Brien W, Hong T, Feng X, Gunay HB, Tahmasebi F, Mahdavi A (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107: 264–278.CrossRefGoogle Scholar
  39. Yan D, Hong T, Dong B, Mahdavi A, D’Oca S, Gaetani I, Feng X (2017). IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings. Energy and Buildings, 156: 258–270.CrossRefGoogle Scholar
  40. Yang X, Tysoe B (2016). Comparing standard domestic hot water modeling methods for multi-residential buildings. In: Proceedings of ASHRAE and IBPSA-USA SimBuild 2016 Building Performance Modeling Conference, Salt Lake City, UT, USA.Google Scholar
  41. Yu Z, Fung BCM, Haghighat F, Yoshino H, Morofsky E (2011). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 43: 1409–1417.CrossRefGoogle Scholar
  42. Yu Z, Fung BCM, Haghighat F (2013). Extracting knowledge from building-related data — A data mining framework. Building Simulation, 6: 207–222.CrossRefGoogle Scholar
  43. Yu Z, Haghighat F, Fung BCM (2016). Advances and challenges in building engineering and data mining applications for energy-efficient communities. Sustainable Cities and Society, 25: 33–38.CrossRefGoogle Scholar
  44. Zhang W, Tan S, Lei Y, Wang S (2014). Life cycle assessment of a single-family residential building in Canada: A case study. Building Simulation, 7: 429–438.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provided a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Antonio Muroni
    • 1
    • 2
  • Isabella Gaetani
    • 1
    • 3
    Email author
  • Pieter-Jan Hoes
    • 1
  • Jan L. M. Hensen
    • 1
  1. 1.Building Physics and ServicesEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Enel S.p.A.RomeItaly
  3. 3.ArupBloomsburyUK

Personalised recommendations