Issue: 2022/Vol.32/No.4, Pages 1-15


Shokhjakhon Abdufattokhov , Nurilla Mahamatov , Kamila Ibragimova, Dilfuza Gulyamova, Dilyorjon Yuldashev

Full paper (PDF)    RePEC

Cite as: S. Abdufattokhov, N. Mahamatov, K. Ibragimova, D. Gulyamova, D. Yuldashev. Supervisory optimal control using machine learning for building thermal comfort. Operations Research and Decisions 2022: 32(4), 1-15. DOI 10.37190/ord220401

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: building thermal comfort, Gaussian processes, machine learning, model predictive control

Received: 01 December 2021    Accepted: 12 December 2022
Published online: 8 February 2023