Robust prediction and MPC-based optimal energy management for HVAC System

Suda, Takatoshi; Namerikawa, Toru

doi:10.1016/j.ifacol.2018.11.182

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Jozef et al solved the temperature control problem of office buildings with characteristics of multi-variable systems, simplified the multivariable system into multiple univariate systems, and designed a decoupled MPC controller, which eliminated the influence of frequent disturbances and improved the energy saving effect [5]. Takatoshi et al used k-means clustering and robust outlier prediction method to predict solar radiation, and used MPC to control the temperature change of HVAC room, so as to obtain comfortable temperature and reduce electricity cost at the same time [6]. Yang et al estimated the uncertain parameters in the building model by using the actual building operation data measured online, introduced the model adaptive function, and designed an adaptive robust controller.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Thermal Comfort Control for Residential Building Based on Nonlinear EMPC

Chang¹,

Kong²,

Li³

et al. 2022

Energy Engineering

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For purpose of achieving the desired thermal comfort level and reducing the economic cost of maintaining the thermal comfort of green residential building, an energy efficient thermal comfort control strategy based on economic model predictive control (EMPC) for green residential buildings which adopts household heat metering is presented. Firstly, the nonlinear thermal comfort model of heating room is analyzed and obtained. A practical nonlinear thermal comfort prediction model is obtained by using an approximation method. Then, the economic cost function and optimization problem of energy-saving under the necessary thermal comfort requirements are constructed to realize the optimal economic performance of the dynamic process. The energy efficient thermal comfort MPC (EETCMPC) is designed. Finally, the comparison and analysis between EETCMPC and Double-layer Model Predictive Control (DMPC) is simulated. The simulation results reveal that when the clothing insulation is typical, the energy efficiency of EETCMPC is 8.9% and 11.6%, respectively, in the two simulation scenarios. When the clothing insulation varies with temperature, the energy efficiency of EETCMPC is 7.29% and 9.15%, respectively, and the total energy consumption is reduced by about 1.65% and 14.6%, respectively, compared with the typical clothing insulation. The economic performance is improved in the thermal comfort dynamic process of heating room.

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Section: Introductionmentioning

confidence: 99%