System identification and model-predictive control of office buildings with integrated photovoltaic-thermal collectors, radiant floor heating and active thermal storage

Li, Siwei; Joe, Jaewan; Hu, Jianjun; Karava, Panagiota

doi:10.1016/j.solener.2014.11.024

Cited by 97 publications

(34 citation statements)

References 37 publications

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“…This fact can be achieved either by including a constant or linear representation of the Coefficient of Performance (COP) of the studied unit in the control-oriented model or in the linear cost function [68,90,100]. In other cases, where the COP is described by a more detailed non-linear function (e.g., non-linearly dependent on system states and disturbances), it can be included in the optimization problem with a non-linear cost function, similarly to the case of the PMV [133,134]. The weighting matrix W ∆u assigns instead a cost to the derivative of the control inputs and therefore their rate of change, ensuring the stability of the system and avoiding excessive fluctuations that can damage the actuators.…”

Section: Control Goals and Objective Functionsmentioning

confidence: 99%

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

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Section: Control Goals and Objective Functionsmentioning

confidence: 99%

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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“…The measurement of the water return temperature allows compensating forecasting errors. In the works of Candanedo and Athienitis [8] and Li et al [11] , the FHS is connected to a solar thermal panel. A simplified building model is obtained from a detailed model.…”

Section: Principle and State Of The Art On Predictive Control Technicmentioning

confidence: 99%

Model predictive control of a thermally activated building system to improve energy management of an experimental building: Part II - Potential of predictive strategy

Viot

Sempey

Mora

et al. 2018

Energy and Buildings

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Thermally Activated Building Systems (TABS) are difficult to control due to the time lag between the control sending and the response of the indoor temperature. Energy management of systems having such a high inertia can be improved by optimizing the restart time thanks to both occupancy and weather anticipation. Predictive control is suitable for systems with numerous constrained inputs and outputs whose objective function varies over time such as buildings with intermittent occupancy. This work proposes to use a Model Predictive Control (MPC) for one TABS in particular: a floor heating system (FHS) of an experimental building. Conventional control techniques and a state of the art on the predictive control of FHS are presented. A complete control loop (sensor, actuator, controller) was implemented on an experimental room. The predictive controller that integrate the model selected in Part I is compared with two conventional control strategies. The energy saving potential of the predictive controller is confirmed by both local experimentation and simulation on three climates. The energy saving is close to 40% over the whole heating season with an improved or equivalent comfort situation compared to the other two reference strategies. The absolute gain is constant over the heating period but the most significant relative gains are obtained in the mid-season.

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“…Besides, the cost of tracer gas is relatively high, especially if the space volume is large. Due to these difficulties, numerous researches have used an assumed constant infiltration rate in their calculations and simulations [13,14]. Hence, a simpler method is urgently needed for assessing infiltration rate.…”

Section: Introductionmentioning

confidence: 99%

A convenient method to assess air infiltration rate using particle mass balance principle

Shi

2019

E3S Web Conf.

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Uncontrolled air leakage across a building exterior (i.e., air infiltration) is a primary reason for excessive energy consumption of air conditioning systems and indoor particle pollution, because infiltration brings outdoor high dew point air and particulate pollutants indoors. Therefore, assessing air infiltration rate is very necessary for improving building energy performance and indoor air quality. However, the commonly used method to assess infiltration rate is very complex to implement. In this paper, we propose a convenient method to assess the infiltration rate based on the mass balance of particulate matters. Given the ubiquity of particulate matters in the outdoor environment and being cost-free, this method is able to avoid the disadvantages of traditional methods. PM2.5 is chosen to establish the mass balance. The method was used in an office room to measure the infiltration rate. Simultaneously, the carbon dioxide tracer gas method was used to verify our method. The relative error between the results calculated by our method and the verifying method is within 10%, which indicates that the proposed method would be feasible in practical applications.

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System identification and model-predictive control of office buildings with integrated photovoltaic-thermal collectors, radiant floor heating and active thermal storage

Cited by 97 publications

References 37 publications

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Model predictive control of a thermally activated building system to improve energy management of an experimental building: Part II - Potential of predictive strategy

A convenient method to assess air infiltration rate using particle mass balance principle

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