Supervisory optimal control using machine learning for building thermal comfort

• Authors: Shokhjakhon Abdufattokhov , Nurilla Mahamatov , Kamila Ibragimova , Dilfuza Gulyamova , Dilyorjon Yuldashev

Abstracts

EN

For the past few decades, control and building engineering communities have been focusing on thermal comfort as a key factor in designing sustainable building evaluation methods and tools. However, estimating the indoor air temperature of buildings is a complicated task due to the nonlinear and complex building dynamics characterised by the time-varying environment with disturbances. The primary focus of this paper is designing a predictive and probabilistic room temperature model of buildings using Gaussian processes (GPs) and incorporating it into model predictive control (MPC) to minimise energy consumption and provide thermal comfort satisfaction. The full probabilistic capabilities of GPs are exploited from two perspectives: the mean prediction is used for the room temperature model, while the uncertainty is involved in the MPC objective not to lose the desired performance and design a robust controller. We illustrated the potential of the proposed method in a numerical example with simulation results.

Keywords

EN

building thermal comfort; Gaussian processes; machine learning; model predictive control

• Publisher: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
• Journal: Operations Research and Decisions
• Year: 2022
• Volume: 32
• Issue: 4

Dates

published

2022

Contributors

author

Shokhjakhon Abdufattokhov

• Department of Automatic Control and Computer Engineering, Turin Polytechnic University in Tashkent, Tashkent, Uzbekistan

author

Nurilla Mahamatov

• Department of Automatic Control and Computer Engineering, Turin Polytechnic University in Tashkent, Tashkent, Uzbekistan

author

Kamila Ibragimova

• Department of Computer Engineering, Tashkent University of Information Technologies, Tashkent, Uzbekistan

author

Dilfuza Gulyamova

• Department of Computer Engineering, Tashkent University of Information Technologies, Tashkent, Uzbekistan

author

Dilyorjon Yuldashev

• Department of Automatic Control and Computer Engineering, Turin Polytechnic University in Tashkent, Tashkent, Uzbekistan

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Identifiers

DOI

10.37190/ord220401

Biblioteka Nauki

2204083