Robust decentralized control of HVAC systems using -performance measures

Al-Assadi, Salem; Patel, Rajni V.; Zaheeruddin, Mohammed; Verma, M.S.; Breitinger, Jens

doi:10.1016/j.jfranklin.2004.06.001

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…Several methods have been developed or proposed for HVAC control; however, traditional techniques such as thermostatic on/off and PID controllers are still the most widely used [9]. Other more sophisticated approaches have been found in literature, including nonlinear controller [10], robust controller [11], and soft control techniques such as those based on fuzzy logic [12] as well as on artificial neural network (ANN) [13]. A dynamic programming approach has also been suggested for the dispatch of on/off HVAC commands to achieve DR in building stock, using a hierarchical approach for multiple unit aggregation [14].…”

Section: Previous Workmentioning

confidence: 99%

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

2021

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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.

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Section: Previous Workmentioning

confidence: 99%

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

2021

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“…The purpose of robust control is to design a controller that works well under time-varying disturbances and changes in parameters. Examples of robust control include supply air temperature control [151], supply airflow rate control [151], and zone temperature control [188].…”

Section: Hard Controlmentioning

confidence: 99%

Modeling and control design of residential HVAC systems for operating cost reduction

Afram¹

2021

Preprint

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The residential HVAC systems in Canada can consume more than 60% of the total energy in a house which results in higher operating costs and environmental pollution. The HVAC is a complex system with variable loads acting on it due to the changes in weather and occupancy. The energy consumption of the HVAC systems can be reduced by adapting to the ever changing loads and implementation of energy conservation strategies along with the appropriate control design. Most of the existing HVAC systems use simple on/off controllers and lack any supervisory controller to reduce the energy consumption and operating cost of the system. In Ontario, due to the variable price of electricity, there is an opportunity to design intelligent control system which can shift the loads to off-peak hours and reduce the operating cost of the HVAC system. In order to take advantage of this opportunity, a supervisory controller based on model predictive control (MPC) was designed in this research. The residential HVAC system models were developed and accurately calibrated with the data measured from the Toronto and Region Conservation Authority’s Archetype Sustainable House, House B (TRCA-ASHB) located in Vaughan, Ontario, Canada. Since HVAC is a large and complex system, it was divided into its major subsystems called energy recovery ventilator (ERV), air handling unit (AHU), radiant floor heating (RFH) system, ground source heat pump (GSHP) and buffer tank (BT). The models of each of the subsystem were developed and calibrated individually. The models were then combined together to develop the model of the whole residential HVAC system. The developed model is able to predict the temperature, flow rate, energy consumption and cost for each individual subsystem and whole HVAC system. The model was used to simulate the performance of the existing HVAC system with on/off controllers and develop the supervisory MPC. The supervisory controller was implemented on the HVAC system of TRCA-ASHB and at least 16% cost savings were verified.

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“…Some centralized MPC-based thermal appliance control schemes for temperature regulation and power consumption reduction were implemented in [2], [8]- [10]. In [11], a robust DMPC based on H ∞ -performance measurement was proposed for HVAC control in a multi-zone building in the presence of disturbance and restrictions. In [13], centralized, decentralized, and distributed controllers based on MPC structure, as well as proportional-integral-derivative (PID) control, were applied to a three-zone building to track the temperature and to reduce the power consumption.…”

Section: Introductionmentioning

confidence: 99%

A Game-Theoretic Decentralized Model Predictive Control of Thermal Appliances in Discrete-Event Systems Framework

Abobakr

Sadid

Zhu

2018

IEEE Trans. Ind. Electron.

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Robust decentralized control of HVAC systems using -performance measures

Cited by 15 publications

References 10 publications

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

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

Modeling and control design of residential HVAC systems for operating cost reduction

A Game-Theoretic Decentralized Model Predictive Control of Thermal Appliances in Discrete-Event Systems Framework

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