Abstract
optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model.
| Original language | English |
|---|---|
| Book series | IFAC-PapersOnLine |
| Volume | 50 |
| Issue number | 1 |
| Pages (from-to) | 7381-7388 |
| Number of pages | 8 |
| ISSN | 2405-8963 |
| DOIs | |
| Publication status | Published - Jul 2017 |
| Event | 2017 IFAC Congress - Duration: 9 Jul 2017 → 14 Jul 2017 https://www.ifac2017.org/ |
Conference
| Conference | 2017 IFAC Congress |
|---|---|
| Period | 09/07/2017 → 14/07/2017 |
| Internet address |
Fingerprint
Keywords
- Modeling for control optimization
- Evolutionary algorithms
- Nonlinear predictive control
Cite this
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Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms. / Vinther, Kasper; Green, Torben; Østergaard, Søren; Bendtsen, Jan Dimon.
In: IFAC-PapersOnLine, Vol. 50, No. 1, 07.2017, p. 7381-7388.Research output: Contribution to journal › Conference article in Journal › Research › peer-review
TY - GEN
T1 - Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms
AU - Vinther, Kasper
AU - Green, Torben
AU - Østergaard, Søren
AU - Bendtsen, Jan Dimon
PY - 2017/7
Y1 - 2017/7
N2 - This paper presents the use a neural network and a micro genetic algorithm tooptimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model.
AB - This paper presents the use a neural network and a micro genetic algorithm tooptimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model.
KW - Modeling for control optimization
KW - Evolutionary algorithms
KW - Nonlinear predictive control
U2 - 10.1016/j.ifacol.2017.08.1477
DO - 10.1016/j.ifacol.2017.08.1477
M3 - Conference article in Journal
VL - 50
SP - 7381
EP - 7388
JO - I F A C Workshop Series
JF - I F A C Workshop Series
SN - 1474-6670
IS - 1
ER -