A Hybrid Physics-Based and Data Driven Approach to Optimal Control of Building Cooling/Heating Systems

Vaghefi, Seyed A.; Jafari, Mohsen A.; Zhu, Jianbo; Brouwer, Jack; Lu, Yuehong

doi:10.1109/tase.2014.2356337

Cited by 43 publications

(19 citation statements)

References 23 publications

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“…Despite the growing diffusion of theoretical works, from Figure 10b it emerges that the number of real buildings actually implementing MPC strategies was relatively small. Up to date, the Illawarra Flame house [67][68][69] in Wollongong and 10 households in Brugg [111], the 3E Headquarters in Brussels [149] and a commercial Building in Allschwil [82,116], a building of the Czech Technical University in Prague [115,153], the UC Merced Campus [62] and the Engineers Construction Engineering Research Laboratory (CERL) in Champaign [152], and the airport of Adelaide [102,150,196] represent the most exciting examples of practical implementation of MPC algorithms in buildings. These applications cover all building classifications-residential, commercial, educational, and other respectively-and are located in various climatic locations.…”

Section: Critical Discussion and Conclusionmentioning

confidence: 99%

“…In the cases where the MPC also model the HVAC system, it is required to manage system set-points and schedules, as well as higher level decisions. For instance, the building and its HVAC system can be set into a specific operating mode (e.g., thermal storage charging or discharging modes, natural or mechanical ventilation modes [66,68,93,98,[100][101][102]). A further classification of MPC architectures must be discussed.…”

Section: Bas and Mpc Control Architecturementioning

confidence: 99%

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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: Critical Discussion and Conclusionmentioning

confidence: 99%

Section: Bas and Mpc Control Architecturementioning

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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“…Thus, due to the first law of thermodynamics [17], the mathematical schema of the PB thermal model which describes the total energy exchange within the thermal zone is given by the following:…”

Section: Physics-based (Pb) Estimation Modelmentioning

confidence: 99%

A Semi-blind Model with Parameter Identification for Building Temperature Estimation

et al. 2017

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An accurate thermal model for building enables the heating system (HS) to work efficiently as well as save energy. Thermal modelling often requires physical parameters of the building, which are difficult to be accurately determined. The aim of this work is to develop an optimal thermal model for better understanding of thermal dynamics with the goal of using this to estimate temperature variation in a few hours ahead within building. Based on the characteristics of thermal motion, a conventional physics-based (PB) model for building temperature estimation is introduced first. Afterwards, in order to refine the model and improve the actual performance, we propose an innovative semi-blind (SB) model based on data-driven approaches. Additionally, the methodologies including self-adaptive algorithms (SAAs) and grey prediction technique (GPT) have been applied in dealing with the integrated parameters estimation (IPE) process to ensure the practicability of the implemented model. The proposed model schema is validated by testing in a laboratory. The results indicate that the proposed approach achieves much higher accuracy in estimating temperature variation than the conventional PB model, with only limited knowledge of the building characteristics. The root mean square deviation (RMSD) of SB model and PB model are 0.18 and 0.43, respectively. According to the results, it can be conclu-

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“…Different control methods for HVAC systems are being used to output the optimal setpoint temperatures. These methods include white-box models (utilizing thermodynamic equations for thermal analysis of HVAC systems and building zones) [9][10][11][12][13], statistical and data-driven methods [14][15][16][17], model predictive control algorithms [18][19][20][21], and artificial intelligence models [18,[22][23][24]. Ghofrani and Jafari presented a physical-statistical approach to optimally control a commercial building air-conditioning operation that is based on building thermal physics and human behavior [25].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

2021

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Cooling systems play a key role in maintaining human comfort inside buildings. The key challenges that are facing conventional cooling systems are the rapid growth of total cooling energy and annual electricity consumption in commercial buildings. This is even more significant in countries with an arid climate, where the cooling systems are typically working 80% of the year. Thus, there has been growing interest in developing smart control models to assign optimal cooling setpoints in recent years. In the present work, we propose an occupancy-based control model that is based on a non-linear optimization algorithm to efficiently reduce energy consumption and costs. The model utilizes a Monte-Carlo method to determine the approximate occupancy schedule for building thermal zones. We compare our proposed model to three different strategies, namely: always-on thermostat, schedule-based model, and a rule-based occupancy-driven model. Unlike these three rule-based algorithms, the proposed optimization approach is a white-box model that considers the thermodynamic relationships in the cooling system to find the optimal cooling setpoints. For comparison, different case studies in five cities around the world were investigated. Our findings illustrate that the proposed optimization algorithm is able to noticeably reduce the cooling energy consumption of the buildings. Significantly, in cities that were located in severe hot regions, such as Doha and Phoenix, cooling energy consumption can be reduced by 14.71% and 15.19%, respectively.

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A Hybrid Physics-Based and Data Driven Approach to Optimal Control of Building Cooling/Heating Systems

Cited by 43 publications

References 23 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

A Semi-blind Model with Parameter Identification for Building Temperature Estimation

The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

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