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Article

An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications

1
Siemens Mobility Limited, Langley Park Way, Chippenham SN15 1GE, UK
2
School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Jae-Weon Jeong and Chi-Ming Lai
Energies 2021, 14(8), 2078; https://doi.org/10.3390/en14082078
Received: 31 January 2021 / Revised: 27 March 2021 / Accepted: 30 March 2021 / Published: 8 April 2021
(This article belongs to the Special Issue Advanced Control Strategies for Buildings and HVAC Systems)
Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) price information, occupant thermal comfort, computational complexity, and nonlinear HVAC dynamics to design a demand response schema. In this paper, a novel two-stage integrated approach for such is proposed and evaluated. A model predictive control (MPC)-based optimiser for supervisory setpoint control is integrated with a digital parameter-adaptive controller for use in a demand response/demand management environment. The optimiser is designed to shift the heating load (and hence electrical load) to off-peak periods by minimising a trade-off between thermal comfort and electricity costs, generating a setpoint trajectory for the inner loop HVAC tracking controller. The tracking controller provides HVAC model information to the outer loop for calibration purposes. By way of calibrated simulations, it was found that significant energy saving and cost reduction could be achieved in comparison to a traditional on/off or variable HVAC control system with a fixed setpoint temperature. View Full-Text
Keywords: HVAC; optimisation; adaptive control; MPC; smart energy; demand response HVAC; optimisation; adaptive control; MPC; smart energy; demand response
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MDPI and ACS Style

Adegbenro, A.; Short, M.; Angione, C. An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications. Energies 2021, 14, 2078. https://doi.org/10.3390/en14082078

AMA Style

Adegbenro A, Short M, Angione C. An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications. Energies. 2021; 14(8):2078. https://doi.org/10.3390/en14082078

Chicago/Turabian Style

Adegbenro, Akinkunmi, Michael Short, and Claudio Angione. 2021. "An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications" Energies 14, no. 8: 2078. https://doi.org/10.3390/en14082078

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