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Appliance use behavior modelling and evaluation in residential buildings: A case study of television energy use

  • Research Article
  • Building Thermal, Lighting, and Acoustics Modeling
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Abstract

Aside from the consumption for domestic cooling and heating, the energy consumption of appliances in a household is a large amount. This energy use by appliances is highly dependent on the activities of the occupants in a building. Occupant behavior in residential buildings possesses the characteristic of randomness, variety, and complexity. More specifically, the profiles of energy consumption and appliance use are highly dependent on the timing of residents’ activities. Thus, it is necessary to model the domestic energy use profile with high temporal resolution. For example, in the context of energy demand response analysis, it is crucial to take occupant behavior into consideration. This article presents a thorough and detailed method for generating an appliance use pattern based on measured power data in real households, taking the television as a case study. The study develops a stochastic model based on appliance use data at one-minute temporal resolution. The proposed model firstly extracts feature parameters to depict the use behavior of the appliance. Then, one stochastic simulation model is established with the input of feature parameters. An evaluation method for this appliance behavior model is also proposed. As a result of one case study simulating appliance consumption at a variety of scales of households, it was concluded that the model can enable applicability to further household energy profile simulation.

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References

  • Baranski M, Voss J (2003). Nonintrusive appliance load monitoring based on an optical sensor. In: Proceedings of IEEE Power Tech Conference, Bologna, Italy.

  • Bartels R, Fiebig DG, Garben M, Lumsdaine R (1992). An end-use electricity load simulation model: Delmod. Utilities Policy, 2: 71–82.

    Google Scholar 

  • Chen Y, Hong T, Luo X (2018). An agent-based stochastic Occupancy Simulator. Building Simulation, 11: 37–49.

    Google Scholar 

  • Cohen AC (1960). Estimating the parameter in a conditional Poisson distribution. Biometrics, 16: 203–211.

    MathSciNet  MATH  Google Scholar 

  • Cole AI, Albicki A (1998). Algorithm for nonintrusive identification of residential appliances. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS’98).

  • Crow EL, Shimizu K (1987). Lognormal Distributions: Theory and Applications. New York: Marcel Dekker.

    MATH  Google Scholar 

  • 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.

    Google Scholar 

  • Dong B, Yan D, Li Z, Jin Y, Feng X, Fontenot H (2018). Modeling occupancy and behavior for better building design and operation—A critical review. Building Simulation, 11: 899–921.

    Google Scholar 

  • Fujimoto T, Yamaguchi Y, Shimoda Y (2017). Energy management for voltage control in a net-zero energy house community considering appliance operation constraints and variety of households. Energy and Buildings, 147: 188–199.

    Google Scholar 

  • Ginos BF (2009). Parameter estimation for the lognormal distribution. Master Thesis, Brigham Young University, USA.

    Google Scholar 

  • Gottwalt S, Ketter W, Block C, Collins J, Weinhardt C (2011). Demand side management—A simulation of household behavior under variable prices. Energy Policy, 39: 8163–8174.

    Google Scholar 

  • Gottwalt S, Garttner J, Schmeck H, Weinhardt C (2017). Modeling and valuation of residential demand flexibility for renewable energy integration. IEEE Transactions on Smart Grid, 8: 2565–2574.

    Google Scholar 

  • Grandjean A, Adnot J, Binet G (2012). A review and an analysis of the residential electric load curve models. Renewable and Sustainable Energy Reviews, 16: 6539–6565.

    Google Scholar 

  • Grahn P, Munkhammar J, Widén J, Alvehag K, Söder L (2013). PHEV home-charging model based on residential activity patterns. IEEE Transactions on Power Systems, 28: 2507–2515.

    Google Scholar 

  • Haight FA (1967). Handbook of the Poisson Distribution. New York: John Wiley & Sons.

    MATH  Google Scholar 

  • Haldi F, Calì D, Andersen RK, Wesseling M, Müller D (2017). Modelling diversity in building occupant behaviour: a novel statistical approach. Journal of Building Performance Simulation, 10: 527–544.

    Google Scholar 

  • Hart GW (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80: 1870–1891.

    Google Scholar 

  • Hoes P, Hensen JLM, Loomans MGLC, de Vries B, Bourgeois D (2009). User behavior in whole building simulation. Energy and Buildings, 41: 295–302.

    Google Scholar 

  • Hong T, Taylor-Lange SC, D’Oca S, Yan D, Corgnati SP (2016). Advances in research and applications of energy-related occupant behavior in buildings. Energy and Buildings, 116: 694–702.

    Google Scholar 

  • Hong T, Yan D, D’Oca S, Chen C-F (2017). Ten questions concerning occupant behavior in buildings: The big picture. Building and Environment, 114: 518–530.

    Google Scholar 

  • Hong T, Chen Y, Belafi Z, D’Oca S (2018). Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs. Building Simulation, 11: 1–14.

    Google Scholar 

  • IEA (2017). Tracking progress: Lighting, appliances and equipment. Available at https://www.iea.org/etp/tracking2017/lightingapplian-cesandequipment/.

  • IEA (2018). Appliances and Equipment. Available at https://www.iea.org/topics/energyefficiency/appliancesandequipment/.

  • Jin Y, Yan D, Zhang X, An J, Han M (2020). A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development. Building Simulation, https://doi.org/10.1007/s12273-020-0638-x.

  • Kong W, Dong Z, Hill DJ, Luo F, Xu Y (2018). Short-term residential load forecasting based on resident behaviour learning. IEEE Transactions on Power Systems, 33: 1087–1088.

    Google Scholar 

  • Lehmann EL, Casella G (2006). Theory of Point Estimation. New York: Springer.

    MATH  Google Scholar 

  • Lesic V, de Bruin WB, Davis MC, Krishnamurti T, Azevedo IML (2018). Consumers’ perceptions of energy use and energy savings: A literature review. Environmental Research Letters, 13: 033004.

    Google Scholar 

  • Li Z, Jiang Y (2006). Investigation methods of air conditioning energy consumption in residential buildings in summer. Journal of HVAC, 36(9): 35–37. (in Chinese)

    Google Scholar 

  • Li J, Just RE (2018). Modeling household energy consumption and adoption of energy efficient technology. Energy Economics, 72: 404–415.

    Google Scholar 

  • Lu X, Feng F, Pang Z, Yang T, O’Neill Z (2020). Extracting typical occupancy schedules from social media (TOSSM) and its integration with building energy modeling. Building Simulation, https://doi.org/10.1007/s12273-020-0637-y.

  • Metropolis N, Ulam S (1949). The Monte Carlo method. Journal of the American Statistical Association, 44: 335–341.

    MathSciNet  MATH  Google Scholar 

  • Norford LK, Leeb SB (1996). Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy and Buildings, 24: 51–64.

    Google Scholar 

  • Paatero JV, Lund PD (2006). A model for generating household electricity load profiles. International Journal of Energy Research, 30: 273–290.

    Google Scholar 

  • Pan S, Wang X, Wei Y, Zhang X, Gal C, et al. (2017). Cluster analysis for occupant-behavior based electricity load patterns in buildings: A case study in Shanghai residences. Building Simulation, 10: 889–898.

    Google Scholar 

  • Patel JK, Read CB (1996). Handbook of the Normal Distribution, 2nd edn. New York: CRC Press.

    MATH  Google Scholar 

  • Powers J, Margossian B, Smith B (1991). Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data. IEEE Computer Applications in Power, 4(2): 42–47.

    Google Scholar 

  • Ren X, Yan D, Wang C (2014). Air-conditioning usage conditional probability model for residential buildings. Building and Environment, 81: 172–182.

    Google Scholar 

  • Richardson I, Thomson M, Infield D, Clifford C (2010). Domestic electricity use: A high-resolution energy demand model. Energy and Buildings, 42: 1878–1887.

    Google Scholar 

  • Shimoda Y, Taniguchi-Matsuoka A, Inoue T, Otsuki M, Yamaguchi Y (2017). Residential energy end-use model as evaluation tool for residential micro-generation. Applied Thermal Engineering, 114: 1433–1442.

    Google Scholar 

  • Stickels T, Markel L (1987). Residential load control and metering equipment: Costs and capabilities.

  • Stokes M (2005). Removing barriers to embedded generation: A finegrained load model to support low voltage network performance analysis. PhD Thesis, De Montfort University, UK.

    Google Scholar 

  • Student (1908). The probable error of a mean. Biometrika, 6: 1–25.

    MATH  Google Scholar 

  • Subbiah R, Pal A, Nordberg EK, Marathe A, Marathe MV (2017). Energy demand model for residential sector: a first principles approach. IEEE Transactions on Sustainable Energy, 8: 1215–1224.

    Google Scholar 

  • Sun K, Hong T (2017). A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures. Energy and Buildings, 146: 383–396.

    Google Scholar 

  • Tang R, Wang S, Sun S (2020). Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use. Building Simulation, https://doi.org/10.1007/s12273-020-0605-6

  • Taniguchi A, Inoue T, Otsuki M, Yamaguchi Y, Shimoda Y, Takami A, Hanaoka K (2016). Estimation of the contribution of the residential sector to summer peak demand reduction in Japan using an energy end-use simulation model. Energy and Buildings, 112: 80–92.

    Google Scholar 

  • Torriti J (2014). A review of time use models of residential electricity demand. Renewable and Sustainable Energy Reviews, 37: 265–272.

    Google Scholar 

  • Walker CF, Pokoski JL (1985). Residential load shape modelling based on customer behavior. IEEE Transactions on Power Apparatus and Systems, PAS-104: 1703–1711.

    Google Scholar 

  • Wang C, Yan D, Jiang Y (2011). A novel approach for building occupancy simulation. Building Simulation, 4: 149–167.

    Google Scholar 

  • Widén J, Lundh M, Vassileva I, Dahlquist E, Ellegård K, Wäckelgård E (2009). Constructing load profiles for household electricity and hot water from time-use data—Modelling approach and validation. Energy and Buildings, 41: 753–768.

    Google Scholar 

  • Widén J, Wäckelgård E (2010). A high-resolution stochastic model of domestic activity patterns and electricity demand. Applied Energy, 87: 1880–1892.

    Google Scholar 

  • Wilke U, Haldi F, Scartezzini J-L, Robinson D (2013). A bottom-up stochastic model to predict building occupants’ time-dependent activities. Building and Environment, 60: 254–264.

    Google Scholar 

  • Yamaguchi Y, Shimoda Y (2014). Validation of an energy demand model of residential buildings. In: Proceedings of the 2nd Asia Conference on International Building Performance Simulation Association.

  • Yamaguchi Y, Shimoda Y (2017). A stochastic model to predict occupants’ activities at home for community-/urban-scale energy demand modelling. Journal of Building Performance Simulation, 10: 565–581.

    Google Scholar 

  • Yamaguchi Y, Yilmaz S, Prakash N, Firth SK, Shimoda Y (2019). A cross analysis of existing methods for modelling household appliance use. Journal of Building Performance Simulation, 12: 160–179.

    Google Scholar 

  • 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.

    Google Scholar 

  • Yan D, Feng X, Jin Y, Wang C (2018). The evaluation of stochastic occupant behavior models from an application-oriented perspective: Using the lighting behavior model as a case study. Energy and Buildings, 176: 151–162.

    Google Scholar 

  • 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.

    Google Scholar 

  • Yao R, Steemers K (2005). A method of formulating energy load profile for domestic buildings in the UK. Energy and Buildings, 37: 663–671.

    Google Scholar 

  • Zhao L, Wang Z (2016). Introduction to Advanced Statistics. Beijing: Higher Education Press. (in Chinese)

    Google Scholar 

  • Zoha A, Gluhak A, Nati M, Imran MA (2013). Low-power appliance monitoring using Factorial Hidden Markov Models. In: Proceedings of IEEE 8th International Conference on the Intelligent Sensors, Sensor Networks and Information Processing.

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Acknowledgements

This study was supported by “the 13th Five-Year” National Key R&D Program of China (No. 2017YFC0702200). Meanwhile, this study was supported by the National Natural Science Foundation of China (No. 51778321). This work was also supported by Science and Technology Project of State Grid of China (Research and Development on Key Technology of Universal Energy Flow Model Based Regional Multi-energy System).

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Authors and Affiliations

  1. School of Architecture, Tsinghua University, Beijing, 100084, China

    Yuan Jin, Da Yan, Hongsan Sun & Jingjing An

  2. State Grid Integrated Energy Planning and D&R Institute, Beijing, 100052, China

    Jieyan Xu, Jianghui Tang & Ruosi Zhang

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  1. Yuan Jin
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  2. Jieyan Xu
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  3. Da Yan
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  4. Hongsan Sun
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  6. Jianghui Tang
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  7. Ruosi Zhang
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Correspondence to Da Yan.

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Jin, Y., Xu, J., Yan, D. et al. Appliance use behavior modelling and evaluation in residential buildings: A case study of television energy use. Build. Simul. 13, 787–801 (2020). https://doi.org/10.1007/s12273-020-0648-8

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  • DOI: https://doi.org/10.1007/s12273-020-0648-8

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