Application of Distributed Model Predictive Approaches to Temperature and CO Concentration Control in Buildings

Walker, Shalika; Lombardi, Warody; Lesecq, Suzanne; Roshany-Yamchi, Samira

doi:10.1016/j.ifacol.2017.08.107

Cited by 17 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Walker et al [21] compared the performance of two methods of MPC, centralized and distributed methods, for indoor temperature and CO 2 level with natural ventilation. Maasoumy et al [22] presented an MPC approach to reduce energy consumption in the HVAC system of a university campus building by controlling the air mass flow rate.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks

Samadi

Shahbakhti

2023

Energies

View full text Add to dashboard Cite

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO2 concentration. The validation results show that the model can predict room temperature and CO2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller.

show abstract

Section: Introductionmentioning

confidence: 99%

“…1. Classification of the control methods in HVAC systems including some of the prior studies[20][21][22][23][24][25][26][27][28][29][30].…”

mentioning

confidence: 99%

Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks

Samadi

Shahbakhti

2023

Energies

View full text Add to dashboard Cite

show abstract

“…Heating, ventilation, and air conditioning (HVAC) systems are responsible for the largest category of end-use energy consumption in buildings [4]. However, the energy savings of HVAC systems should not have an adverse effect on occupants' health or welfare, since the comfort of the occupants may be negatively affected by reducing the energy used by the HVAC [5].…”

Section: Introductionmentioning

confidence: 99%

“…MPC uses a model to predict the future states of a system and generates a control vector that minimizes a certain cost function over the prediction horizon in the presence of disturbances and constraints [16]. Additionally, the MPC scheme has been widely used for HVAC control to improve IAQ and thermal comfort as well as achieve energy savings [5,[17][18][19]. The MPC scheme consists of a prediction model, an objective function for the optimization problem, and a control law with constraints [14].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model for Forecasting Indoor CO2 Concentration

et al. 2022

View full text Add to dashboard Cite

Indoor CO2 concentration is considered a metric of indoor air quality that affects the health of occupants. In this study, a hybrid model was developed for forecasting the varying indoor CO2 concentration levels in a residential apartment unit in the presence of occupants by controlling the ventilation rates of a heat recovery ventilator. In this model, the mass balance equation for a single zone as a white-box model was combined with a Bayesian neural network (BNN) as a black box model. During the learning process of the hybrid model, the BNN estimated an aggregated unknown ventilation rate and transferred the estimation to the mass-balance equation. A parametric study was conducted by changing the prediction horizons of the hybrid model from 5 to 15 min, and the forecasting performance of the hybrid model was compared with the stand-alone mass balance equation. The hybrid model showed better forecasting performance than that of the mass balance equation on the experimental dataset for a living room and bedroom. The average MBE and CVRMSE of the hybrid model for the prediction horizon of 15 min were 0.65% and 5.23%, respectively, whereas those of the mass balance equation were 0.99% and 9.30%, respectively.

show abstract

“…Also, the research focuses on planning level with 1-hour sampling time. Focusing on distributed MPC (DMPC) applied to the building control problem, [24] has discussed a noniterative, non-cooperative DMPC algorithm based on the classification discussed in [25]; in the meanwhile the algorithms in [26], [27], [28] belong to class of distributed optimization for the centralized optimization problem employing different methods such as the proximal Jacobian alternating direction method of multipliers (ADMM) in [26], the primal-dual active set in [27], the Lagrangian dual method in [28]. In the same class of distributed optimization for the centralized optimization problem, the incremental proximal method is discussed in [29]; the method is generally believed as a more stable approach than the gradientbased one.The present paper addresses the problem of management and coordination of energy resources in a typical microgrid, including flexible loads, energy storages and renewables.…”

mentioning

confidence: 99%

A hierarchical distributed predictive control approach for microgrids energy management

Dao

Dehghani-Pilehvarani

Markou

et al. 2019

Sustainable Cities and Society

View full text Add to dashboard Cite

This paper addresses the problem of management and coordination of energy resources in a typical microgrid, including smart buildings as flexible loads, energy storages and renewables. The overall goal is to provide a comprehensive and innovative framework to maximize the overall benefit, still accounting for possible requests to change the load profile coming from the grid and leaving every single building or user to balance between servicing those requests and satisfying his own comfort levels. The user involvement in the decision-making process is granted by a management and control solution exploiting an innovative distributed model predictive control approach with coordination. In addition, also a hierarchical structure is proposed, to integrate the distributed MPC user-side with the microgrid control, also implemented with an MPC technique. The proposed overall approach has been implemented and tested in several experiments in the laboratory facility for distributed energy systems (Smart RUE) at NTUA, Athens, Greece. Simulation analysis and results complement the testing, showing the accuracy and the potential of the method, also in the perspective of implementation. Index Terms-Energy Management, Distributed Control, Model Predictive Control, Microgrid(MPC), which is well suited to deal with a large amount of constraints of different types that have to be imposed in real time in microgrids. This technique has been exploited for example in [2,3,11,12] and [17][18][19][20][21][22][23]. In [2], [3] and [21] the flexible loads are modelled as a predefined range of possible load consumption and the microgrid controller can directly determine a load profile as long as it fulfils the range. This approach neglects the dynamic of load flexibility in the shiftable loads category (e.g., heating/cooling system, refrigerator, washing machine, etc.) since the size of the predefined range in one step of this load type can not be fixed in advance and it depends on the value of decision variable in the previous steps. On the other hand, in [17] and [23], the load side may reveal its model which later will be formulated in the optimization problem in a centralized controller.Concerning the control scheme, a centralized predictive scheme is considered in [2], [3], [12], [17] and [18]. However, it is well-known that this scheme presents issues of scalability, computational burden, failure of single unit, adaptability, etc. Recent works are putting more attention to the distributed MPC and hierarchical control schemes, such as [11], [19]. In particular, in [19], a two-layer control scheme based MPC operating at two different timescales has been studied. In the paper, some details on the markets (e.g., imbalance charge, difference in purchasing and selling tariffs) are neglected and the flexible load is not considered. On the other hand, the paper [11] employs a sequential distributed MPC on energy management problems in the microgrid; however, some details are missing, including the presence of RES (Renewable Energy Resource),...

show abstract

Application of Distributed Model Predictive Approaches to Temperature and CO Concentration Control in Buildings

Cited by 17 publications

References 7 publications

Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks

Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks

Hybrid Model for Forecasting Indoor CO2 Concentration

A hierarchical distributed predictive control approach for microgrids energy management

Contact Info

Product

Resources

About