Novel Modeling and Control Strategies for a HVAC System Including Carbon Dioxide Control

Kang, Chang-Soon; Park, Jong-Il; Park, Mignon; Baek, Jaeho

doi:10.3390/en7063599

Cited by 30 publications

(21 citation statements)

References 14 publications

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“…Our problem model is motivated by several practical scenarios, e.g., smart building and load monitoring in smart grid. For both of these two applications, the physical dynamics can be approximated by a linear dynamic model (Kang et al, 2014;Maruta and Takarada, 2014). In smart building, a system operator (data requester) uses temperature data of individual rooms (agents) to monitor working comfortability and energy usage conditions.…”

Section: Adversary Modelmentioning

confidence: 99%

On privacy preserving data release of linear dynamic networks

Lü

Zhu

2020

Automatica

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Distributed data sharing in dynamic networks is ubiquitous. It raises the concern that the private information of dynamic networks could be leaked when data receivers are malicious or communication channels are insecure. In this paper, we propose to intentionally perturb the inputs and outputs of a linear dynamic system to protect the privacy of target initial states and inputs from released outputs. We formulate the problem of perturbation design as an optimization problem which minimizes the cost caused by the added perturbations while maintaining system controllability and ensuring the privacy. We analyze the computational complexity of the formulated optimization problem. To minimize the 0 and 2 norms of the added perturbations, we derive their convex relaxations which can be efficiently solved. The efficacy of the proposed techniques is verified by a case study on a heating, ventilation, and air conditioning system.

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Section: Adversary Modelmentioning

confidence: 99%

On privacy preserving data release of linear dynamic networks

Lü

Zhu

2020

Automatica

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“…The optimization problem must be defined as robust from the perspective of model uncertainty and noise, namely, its design must not contain any strict constraints on the output variable, as defined by Equation (16) [32]. The said restrictions may, owing to the effect of the stochastic component, lead to the infeasibility of the model.…”

Section: Thermostatically Controlled Appliancesmentioning

confidence: 99%

“…Besides the temperature and humidity, the actual user comfort is, as indicated above, markedly influenced by the concentration of CO 2 . A model respecting such concentration is optimized in [16].…”

Section: Thermostatically Controlled Appliancesmentioning

confidence: 99%

“…In addition to basic temperature regulation, current HVAC systems consider also the air humidity and, in some cases, carbon dioxide levels in the home [16]. The systems are being developed for two main purposes: First, the research and development of appliances to achieve more effective use of energy [17]; second, the investigation of methods to control the discussed systems [18][19][20].…”

Section: Thermostatically Controlled Appliancesmentioning

confidence: 99%

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A Heuristic Algorithm to Compute Multimodal Criterial Function Weights for Demand Management in Residential Areas

2017

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Abstract:We present the conceptual design of a collective control scheme for appliances within a smart home. Based on the relevant energy acquisition procedures, three appliance groups are defined, modeled, and completed with an energy storage as well as a generator using renewable sources. At the following stage, a mixed quadratic optimization problem is presented, with the solution consisting in a time plan to regulate the operation of the individual devices. Importantly, the paper also proposes a heuristic algorithm securing consistent functionality of the computational process even despite the varying input and user conditions given in the receding horizon.

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“…According to [35], a modeling and control strategy for a novel HVAC system is presented in a way that considers temperature, humidity ratio and CO 2 concentration; the HVAC system is Multi-Input Multi-Output (MIMO); thus, a dynamic extension algorithm can be employed, and a feedback linearization technique is applied. A Linear-Quadratic Regulator (LQR) is designed to optimize control performance and to stabilize the proposed HVAC system.…”

Section: Related Workmentioning

confidence: 99%

Smart Control System to Optimize Time of Use in a Solar-Assisted Air-Conditioning by Ejector for Residential Sector

et al. 2018

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Abstract:The present work provides a series of theoretical improvements of a control strategy in order to optimize the time of use of solar air-conditioning by an ejector distributed in multiple solar collectors of vacuum tubes for the residential sector, which will allow us to reduce carbon-dioxide emissions, costs and electrical energy consumption. In a solar ejector cooling system, the instability of the solar source of energy causes an operational conflict between the solar thermal system and ejector cooling cycle. A fuzzy control structure for the supervisory ejector cycle and multi-collector control system is developed: the first control is applied to control the mass flow of the generator and the evaporator for different cooling capacities (3, 3.5, 4, 4.5 and 5 kW) and set a temperature reference according to the operating conditions; the second is applied to keep a constant temperature power source that feeds the low-grade ejector cooling cycle using R134aas refrigerant. For the present work, the temperature of the generator oscillates between 65 • C and 90 • C, a condenser temperature of 30 • C and an evaporator temperature of 10 • C. For the purpose of optimization, there are different levels of performance for time of use: the Mode 0 (economic) gives a performance of 17.55 h, Mode 5 (maximum cooling power) 14.86 h and variable mode (variable mode of capacities) 16.25 h, on average. Simulations are done in MATLAB-Simulink applying fuzzy logic for a mathematical model of the thermal balance. They are compared with two different types of solar radiation: real radiation and disturbed radiation.

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Novel Modeling and Control Strategies for a HVAC System Including Carbon Dioxide Control

Cited by 30 publications

References 14 publications

On privacy preserving data release of linear dynamic networks

On privacy preserving data release of linear dynamic networks

A Heuristic Algorithm to Compute Multimodal Criterial Function Weights for Demand Management in Residential Areas

Smart Control System to Optimize Time of Use in a Solar-Assisted Air-Conditioning by Ejector for Residential Sector

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