Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

Kasper Vinther, Torben Green, Søren Østergaard, Jan Dimon Bendtsen

Research output: Contribution to journalConference article in JournalResearchpeer-review

4 Citations (Scopus)
175 Downloads (Pure)


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.
Original languageEnglish
Book seriesIFAC-PapersOnLine
Issue number1
Pages (from-to)7381-7388
Number of pages8
Publication statusPublished - Jul 2017
Event2017 IFAC Congress -
Duration: 9 Jul 201714 Jul 2017


Conference2017 IFAC Congress
Internet address


  • Modeling for control optimization
  • Evolutionary algorithms
  • Nonlinear predictive control

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