@article{01H39JZAA256532QE6A9Y645AR,
  abstract     = {{Temperature control in buildings has been a highly studied area of research and interest since it affects the comfort of occupants. Commonly, temperature systems like centralized air conditioning or heating systems work with a fixed set point locally set at the thermostat, but users turn on or turn off the system when they feel it is too hot or too cold. This configuration is clearly not optimal in terms of energy consumption or even thermal comfort for users. Model predictive control (MPC) has been widely used for temperature control systems. In MPC design, the objective function involves the selection of constant weighting factors. In this study, a fractional-order objective function is implemented, so the weighting factors are time-varying. Furthermore, we compared the performance and disturbance rejection of MPC and Fractional-order MPC (FOMPC) controllers. To this end, we have chosen a building model from an EnergyPlus repository. The weather data needed for the EnergyPlus calculations has been obtained as a licensed file from the ASHRAE Handbook. Furthermore, we acquired a mathematical model by employing the Matlab system identification toolbox with the data obtained from the building model simulation in EnergyPlus. Next, we designed several FOMPC controllers, including the classical MPC controllers. Subsequently, we ran co-simulations in Matlab for the FOMPC controllers and EnergyPlus for the building model. Finally, through numerical analysis of several performance indexes, the FOMPC controller showed its superiority against the classical MPC in both reference tracking and disturbance rejection scenarios.}},
  articleno    = {{172}},
  author       = {{Cajo Diaz, Ricardo Alfredo and Zhao, Shiquan and Birs, Isabela Roxana and  Espinoza, Victor and Fernandez Cornejo, Edson Rodrigo and Plaza Guingla, Douglas Antonio and  Salcan-Reyes, Gabriela}},
  issn         = {{2504-3110}},
  journal      = {{FRACTAL AND FRACTIONAL}},
  keywords     = {{EnergyPlus,MLE plus,building simulation,energy-efficient building,model predictive control,fractional order cost function,MODEL-PREDICTIVE CONTROL,NEURAL-NETWORK,BIFURCATIONS,SYSTEM}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{13}},
  title        = {{An advanced fractional order method for temperature control}},
  url          = {{http://doi.org/10.3390/fractalfract7020172}},
  volume       = {{7}},
  year         = {{2023}},
}

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