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Data-driven simulation for energy consumption estimation in a smart home

Author

Listed:
  • Stephen Adams

    (University of Virginia)

  • Steven Greenspan

    (CA Technologies)

  • Maria Velez-Rojas

    (CA Technologies)

  • Serge Mankovski

    (CA Technologies)

  • Peter A. Beling

    (University of Virginia)

Abstract

Simulation and data-driven models are both tools that can play an important role in reducing the energy consumption of buildings and homes. However, sophisticated control schemes and models are only as good as the data collected by sensors and provided to them. Low-quality or faulty sensor that provide inaccurate data can lead to inefficient buildings. In this paper, we investigate the relationship between sensor quality and the prediction of energy consumption. We first construct a simulation of appliance energy consumption in a smart home and then assess the predictive ability of several data-driven models while varying the quality and function of the simulated sensors. The simulation was constructed using a smart home data set collected by other researchers. We find that the predictive ability is only decreased when noise is added to the appliance energy random variable. We conclude that low-quality sensors that do not monitor the environment as accurately as the devices used in the original study could be used for humidity and temperature without significantly reducing the predictive ability of the data-driven models. The method and findings have implications for how to conduct cost-benefit analyses of IoT device requirements.

Suggested Citation

  • Stephen Adams & Steven Greenspan & Maria Velez-Rojas & Serge Mankovski & Peter A. Beling, 2019. "Data-driven simulation for energy consumption estimation in a smart home," Environment Systems and Decisions, Springer, vol. 39(3), pages 281-294, September.
    • Handle: RePEc:spr:envsyd:v:39:y:2019:i:3:d:10.1007_s10669-019-09727-1
    DOI: 10.1007/s10669-019-09727-1
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    References listed on IDEAS

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    1. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    2. Costa, Andrea & Keane, Marcus M. & Torrens, J. Ignacio & Corry, Edward, 2013. "Building operation and energy performance: Monitoring, analysis and optimisation toolkit," Applied Energy, Elsevier, vol. 101(C), pages 310-316.
    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.
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    Cited by:

    1. Zachary A. Collier & James H. Lambert & Igor Linkov, 2019. "Advances in machine learning and decision making," Environment Systems and Decisions, Springer, vol. 39(3), pages 247-248, September.
    2. Bokolo Anthony Jnr & Sobah Abbas Petersen, 2023. "Using an extended technology acceptance model to predict enterprise architecture adoption in making cities smarter," Environment Systems and Decisions, Springer, vol. 43(1), pages 36-53, March.
    3. Bokolo Anthony, 2023. "Decentralized brokered enabled ecosystem for data marketplace in smart cities towards a data sharing economy," Environment Systems and Decisions, Springer, vol. 43(3), pages 453-471, September.
    4. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.

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