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A Stochastic Model for Residential User Activity Simulation

Author

Listed:
  • Xiufeng Liu

    (Department of Management Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark)

  • Yanyan Yang

    (School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK)

  • Rongling Li

    (Department of Civil Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark)

  • Per Sieverts Nielsen

    (Department of Management Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark)

Abstract

User activities is an important input to energy modelling, simulation and performance studies of residential buildings. However, it is often difficult to obtain detailed data on user activities and related energy consumption data. This paper presents a stochastic model based on Markov chain to simulate user activities of the households with one or more family members, and formalizes the simulation processes under different conditions. A data generator is implemented to create fine-grained activity sequences that require only a small sample of time-use survey data as a seed. This paper evaluates the data generator by comparing the generated synthetic data with real data, and comparing other related work. The results show the effectiveness of the proposed modelling approach and the efficiency of generating realistic residential user activities.

Suggested Citation

  • Xiufeng Liu & Yanyan Yang & Rongling Li & Per Sieverts Nielsen, 2019. "A Stochastic Model for Residential User Activity Simulation," Energies, MDPI, vol. 12(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3326-:d:261870
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    References listed on IDEAS

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    1. Santiago, I. & López-Rodríguez, M.A. & Gil-de-Castro, A. & Moreno-Munoz, A. & Luna-Rodríguez, J.J., 2013. "Energy consumption of audiovisual devices in the residential sector: Economic impact of harmonic losses," Energy, Elsevier, vol. 60(C), pages 292-301.
    2. Marszal-Pomianowska, Anna & Heiselberg, Per & Kalyanova Larsen, Olena, 2016. "Household electricity demand profiles – A high-resolution load model to facilitate modelling of energy flexible buildings," Energy, Elsevier, vol. 103(C), pages 487-501.
    3. Muratori, Matteo & Roberts, Matthew C. & Sioshansi, Ramteen & Marano, Vincenzo & Rizzoni, Giorgio, 2013. "A highly resolved modeling technique to simulate residential power demand," Applied Energy, Elsevier, vol. 107(C), pages 465-473.
    4. Torriti, Jacopo, 2012. "Demand Side Management for the European Supergrid: Occupancy variances of European single-person households," Energy Policy, Elsevier, vol. 44(C), pages 199-206.
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    Cited by:

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    2. Roche, Steven, 2020. "Conceptualising children’s life histories and reasons for entry into residential care in the Philippines: Social contexts, instabilities and safeguarding," Children and Youth Services Review, Elsevier, vol. 110(C).
    3. Jonas Bielskus & Violeta Motuzienė & Tatjana Vilutienė & Audrius Indriulionis, 2020. "Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model," Energies, MDPI, vol. 13(15), pages 1-20, August.
    4. Máté János Lőrincz & José Luis Ramírez-Mendiola & Jacopo Torriti, 2021. "Impact of Time-Use Behaviour on Residential Energy Consumption in the United Kingdom," Energies, MDPI, vol. 14(19), pages 1-32, October.

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