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Grey-Box Energy Modelling of Energy-Efficient House Using Hybrid Optimization Technique of Genetic Algorithms (GA) and Quasi-Newton Algorithms with Markov Chain Monte Carlo Uncertainty Distribution

Author

Listed:
  • Gulsun Demirezen

    (Department of Mechanical and Industrial Engineering, Toronto Metropolitan University, 350 Victoria St., Toronto, ON M5B2K3, Canada)

  • Alan S. Fung

    (Department of Mechanical and Industrial Engineering, Toronto Metropolitan University, 350 Victoria St., Toronto, ON M5B2K3, Canada)

  • Aidan Brookson

    (Department of Mechanical and Industrial Engineering, Toronto Metropolitan University, 350 Victoria St., Toronto, ON M5B2K3, Canada)

Abstract

Understanding energy demands and costs is important for policy makers and the energy sector, especially in the context of residential heating and cooling systems. To estimate the thermal demand of a residential house, a grey-box modelling method with a resistance–capacitance (RC) analogy was implemented. The architectural properties used to parameterize the grey-box model were derived from a house used for research purposes in Vaughan, Ontario, Canada (TRCA-House A). The house model accounts for solar irradiance on exterior building surfaces, thermal conductivity through all surfaces, solar heat gains through windows, and thermal gains from ventilation. Two parallel short- and long-term calibrations were performed such that model outputs reflected the real-world operation of the house as best as possible. To define the unknown model parameters (such as the conductivity of building materials and some constant parameters), a hybrid optimization scheme including a genetic algorithm (GA) and the Quasi-Newton algorithm was introduced and implemented using Bayesian approximation and Markov Chain Monte Carlo (MCMC) methods. The temperature outputs from the model were compared to the data retrieved from TRCA-House A. The final iteration of the model had an RMSE for interior zone temperature estimation of 0.22 °C when compared to the retrieved interior zone temperature data from TRCA-House A. Furthermore, the annual heating and cooling energy consumption values are within 1.50% and 0.08% of target values, respectively. According to these preliminary results, the introduced model and optimization techniques could be adjusted for different types of housing, as well as for smart control applications on both a short- and long-term basis.

Suggested Citation

  • Gulsun Demirezen & Alan S. Fung & Aidan Brookson, 2024. "Grey-Box Energy Modelling of Energy-Efficient House Using Hybrid Optimization Technique of Genetic Algorithms (GA) and Quasi-Newton Algorithms with Markov Chain Monte Carlo Uncertainty Distribution," Energies, MDPI, vol. 17(23), pages 1-30, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5941-:d:1530078
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    References listed on IDEAS

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