A smart heating set point scheduler using an artificial neural network and genetic algorithm

This paper introduces a novel, adaptive, heating set point scheduler that aims to minimise the heating energy consumption in a building while maintaining thermal comfort levels. The presented control strategy couples two computational intelligence techniques, an Artificial Neural Network, ANN and a...

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Bibliographic Details
Published in23rd ICE/ITMC : 2017 International Conference on Engineering, Technology and Innovation : conference proceedings : 27-29 June 2017, Madeira Island, Portugal pp. 704 - 710
Main Authors Reynolds, Jonathan, Hippolyte, Jean-Laurent, Rezgui, Yacine
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2017
Subjects
Artificial Intelligence
Building Control
Buildings
Computational modeling
Data models
Energy
Energy consumption
Heating systems
HVAC
Mathematical model
Optimization
Smart Buildings
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DOI10.1109/ICE.2017.8279954

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Summary:This paper introduces a novel, adaptive, heating set point scheduler that aims to minimise the heating energy consumption in a building while maintaining thermal comfort levels. The presented control strategy couples two computational intelligence techniques, an Artificial Neural Network, ANN and a Genetic Algorithm, GA. The ANN surrogate model was trained using data from multiple Energy Plus building simulations with varied heating set points. This allows quick, computationally cheap, calculation of a fitness function, opposed to time consuming Energy Plus simulations. The ANN's inputs include weather and occupancy predictions as well as taking account of the buildings thermal inertia to predict energy consumption, predicted percentage dissatisfied, PPD, and indoor temperature. A multi-objective GA uses the ANN to calculate the energy sum over 24 hours and the average occupied PPD as its two objectives. The optimisation strategy was applied on a week in January over which it reduced energy consumption by 4.93% and improved PPD by 0.76%. The great advantage of this controller is that it could be simply adapted to take account of the changing energy environment. This could include an addition of renewable resources or demand response controls as part of a district heating network.
DOI:10.1109/ICE.2017.8279954