Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Kamila Ibragimova
Tashkent University of Information Technologies

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Predictive and probabilistic modelling using machine learning for building indoor climate control Shokhjakhon Abdufattokhov; Nurilla Mahamatov; Kamila Ibragimova; Dilfuza Gulyamova
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1306-1314

Abstract

For the last few decades, thermal comfort has been considered an aspect of sustainable building evaluation methods and tools. However, estimating the indoor air temperature of buildings is a complicated task due to the nonlinear behaviour of heating, ventilation and air conditioning systems combined with complex dynamics characterized by the time-varying environment with disturbances. This issue can be alleviated by modelling the building dynamics using Gaussian processes since it also measures the uncertainty bounds. The main focus of this paper is designing a predictive and probabilistic room temperature model of buildings using Gaussian processes and incorporating it into model predictive control to minimize energy consumption and provide thermal comfort satisfaction. We exploited the Gaussian processes’ full probabilistic capabilities as the mean prediction for the room temperature model and used the model uncertainty in the objective function not to lose the desired performance and to design a robust control scheme. We illustrated the potentials of the proposed method in a numerical example with simulation results.
Co-Authors Dilfuza Gulyamova Nurilla Mahamatov Shokhjakhon Abdufattokhov