Abstract
This paper presents the use a neural network and a micro genetic algorithm to
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.
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 |
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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 |
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Period | 09/07/2017 → 14/07/2017 |
Internet address |
Keywords
- Modeling for control optimization
- Evolutionary algorithms
- Nonlinear predictive control