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- W4386012712 abstract "Online optimization of building thermal environment is no trivial task due to the complex and uncertain indoor thermodynamics. In this study, an extended Kalman filter-based extremum seeking control (EKF-ESC) strategy is developed for model-free control of thermal environment under uncertainty. The EKF-ESC uses extended Kalman filter for estimating gradient of the objective function of the extremum seeking controller, which enables real-time optimization with fast convergence time. In addition, a co-simulation platform is developed by combining EnergyPlus and Simulink/Matlab for validating effectiveness of the EKF-ESC strategy for controlling thermal environment. Our study demonstrates that the EKF-ESC strategy has good adaptivity to uncertainties resulting from changes in weather, electrical equipment and occupancy. Moreover, it is shown that the EKF-ESC is robust to measurement noise with strong noise-tolerating capability. In addition, comparison of convergence performance among EKF-ESC, adaptive ESC, and classical ESC is conducted. It is shown that the EKF-ESC based control strategy has the fastest response time towards indoor temperature setpoints. Compared with the classical ESC and adaptive ESC schemes, about 77% and 54% reduction of the convergence time are realized through EKF-ESC. According to the research results, it could be inferred that EKF-ESC strategy has the potential for effective building thermal management under uncertainty with fast convergence time." @default.
- W4386012712 created "2023-08-21" @default.
- W4386012712 creator A5052263501 @default.
- W4386012712 creator A5072985650 @default.
- W4386012712 date "2023-11-01" @default.
- W4386012712 modified "2023-10-02" @default.
- W4386012712 title "Extended Kalman filter based extremum seeking algorithm for model-free control of building thermal environment under uncertainty" @default.
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- W4386012712 doi "https://doi.org/10.1016/j.jobe.2023.107621" @default.
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