Physics-Enhanced Machine Learning for Energy Systems
(2022)Department of Automatic Control
- Abstract
- Building operations account for a large amount of energy usage and the HVAC (Heating, Ventilation and Air Conditioning) systems are the largest consumer of energy in this sector. To reduce this demand, more energy-efficient control algorithms are implemented and a popular choice for a controller is the model predictive control. However, this demands a precise model. Modeling the indoor climate in buildings is a difficult task since a lot of disturbances affect the process. Some of these disturbances are also unmeasured such as emitted heat from computers and various human patterns.
This thesis aims to use data-driven methods to find suitable procedures to model the indoor climate in buildings. This is done in two steps. First, a gray... (More) - Building operations account for a large amount of energy usage and the HVAC (Heating, Ventilation and Air Conditioning) systems are the largest consumer of energy in this sector. To reduce this demand, more energy-efficient control algorithms are implemented and a popular choice for a controller is the model predictive control. However, this demands a precise model. Modeling the indoor climate in buildings is a difficult task since a lot of disturbances affect the process. Some of these disturbances are also unmeasured such as emitted heat from computers and various human patterns.
This thesis aims to use data-driven methods to find suitable procedures to model the indoor climate in buildings. This is done in two steps. First, a gray box model is created and its parameters fitted using different data-driven methods. Then, more complex learning-based models are applied and added to the gray box part to catch some of the unmeasured disturbances. Feed-forward neural networks, LSTM networks and ARX models are methods used for this unmeasured disturbance modelling part of the project.
The results showed that a gray box model can capture most of the dynamics of the heat flow in buildings, although the obtained parameters and the performance depended a lot of the method used for parameter estimation. Adding a more complexdisturbance part to the gray box model improved the results significantly as it allowed for unmeasured disturbances to be taken into consideration. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9094392
- author
- Lindström, Henrik and Sundström, Emil
- supervisor
-
- Felix Agner LU
- Anders Rantzer LU
- Bo Bernhardsson LU
- organization
- year
- 2022
- type
- H3 - Professional qualifications (4 Years - )
- subject
- report number
- TFRT-6163
- ISSN
- 0280-5316
- language
- English
- id
- 9094392
- date added to LUP
- 2022-08-12 09:58:12
- date last changed
- 2022-08-12 09:58:12
@misc{9094392,
abstract = {{Building operations account for a large amount of energy usage and the HVAC (Heating, Ventilation and Air Conditioning) systems are the largest consumer of energy in this sector. To reduce this demand, more energy-efficient control algorithms are implemented and a popular choice for a controller is the model predictive control. However, this demands a precise model. Modeling the indoor climate in buildings is a difficult task since a lot of disturbances affect the process. Some of these disturbances are also unmeasured such as emitted heat from computers and various human patterns.
This thesis aims to use data-driven methods to find suitable procedures to model the indoor climate in buildings. This is done in two steps. First, a gray box model is created and its parameters fitted using different data-driven methods. Then, more complex learning-based models are applied and added to the gray box part to catch some of the unmeasured disturbances. Feed-forward neural networks, LSTM networks and ARX models are methods used for this unmeasured disturbance modelling part of the project.
The results showed that a gray box model can capture most of the dynamics of the heat flow in buildings, although the obtained parameters and the performance depended a lot of the method used for parameter estimation. Adding a more complexdisturbance part to the gray box model improved the results significantly as it allowed for unmeasured disturbances to be taken into consideration.}},
author = {{Lindström, Henrik and Sundström, Emil}},
issn = {{0280-5316}},
language = {{eng}},
note = {{Student Paper}},
title = {{Physics-Enhanced Machine Learning for Energy Systems}},
year = {{2022}},
}