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Temperature Control of a Commercial Building With Model Predictive Control Techniques

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Abstract

This paper addresses the problem of thermal energy control in shopping centers through the application of model predictive control (MPC) strategies. In particular, this paper uses an existing shopping center as pilot case, which is characterized by a large common multifloor space, which, in turn, gives rise to a significant vertical thermal stratification. This paper explores the importance of MPC parameters to energy efficiency and comfort levels. In addition, it addresses some notable extensions, from economic optimization, which paves the way to real-time pricing techniques, to integration with renewables, to robustness enforcement through Kalman filters, to hybrid control with switching signals mixed with continuous signals.

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... Traditionally, MPC is used for a single-agent system, where the control input is obtained by numerically optimizing a finite horizon optimal control problem where both nonlinearity and constraints can be explicitly handled [23]. This technique has been embraced by many industrial applications, for instance, thermal energy control [24], collision avoidance [18], and vehicle stability [25], and energy management [26], etc. Most of these MPCs are implemented in a centralized way, where all the control inputs are computed by assuming all the states are known, e.g., [18][24]- [26]. ...
... (24) Since ( + ) −1 is non-negative, one of its eigenvalues is equal to one according to (24) [35]. Let the corresponding eigenvector be , then the following equality holds. ...
Preprint
This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between predicted and assumed trajectories. Together with this penalization, an equality based terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of proposed DMPC.
... Accordingly, various models and control methods have been developed to reduce energy consumption, energy cost, or thermal discomfort. For example, an HVAC control method based on MPC (Model Predictive Control) techniques was proposed in [14] to reduce vertical thermal stratification and discomfort due to overheating in a commercial building. In [15], Ma et al. presented a stochastic MPC-based HVAC control method to minimize the expected energy cost while bounding the probability of thermal comfort violations by exploiting stochastic information of weather and load learned from historical data. ...
... To begin with, three assumptions are made about system parameters so that the system is controllable, i.e., (12)- (14). (12) implies that the temperature decrease of zone i can be stopped by setting the minimum air rate m min i given minimum indoor temperature T min i , minimum outdoor temperature T min o , and minimum external disturbance q min i = min t q i,t . ...
Preprint
In this paper, we investigate the problem of minimizing the long-term total cost (i.e., the sum of energy cost and thermal discomfort cost) associated with a Heating, Ventilation, and Air Conditioning (HVAC) system of a multizone commercial building under smart grid environment. To be specific, we first formulate a stochastic program to minimize the time average expected total cost with the consideration of uncertainties in electricity price, outdoor temperature, the most comfortable temperature level, and external thermal disturbance. Due to the existence of temporally and spatially coupled constraints as well as unknown information about the future system parameters, it is very challenging to solve the formulated problem. To this end, we propose a realtime HVAC control algorithm based on the framework of Lyapunov optimization techniques without the need to predict any system parameters and know their stochastic information. The key idea of the proposed algorithm is to construct and stabilize virtual queues associated with indoor temperatures of all zones. Moreover, we provide a distributed implementation of the proposed realtime algorithm with the aim of protecting user privacy and enhancing algorithmic scalability. Extensive simulation results based on real-world traces show that the proposed algorithm could reduce energy cost effectively with small sacrifice in thermal comfort.
... B) Model Predictive Control-based BEMS: An alternative energy management methodology is using Model Predictive Control (MPC), which uses the learning power of Machine Learning (ML) algorithms to predict potential outcomes of the energy management systems. MPC has made significant progress in recent years, leading to the development of sophisticated HVAC control policies and algorithms that have shown promising results in energy optimization [8,9,10]. The MPC approach involves creating a digital twin of the real building, which closely mirrors the building structure and control logic. ...
... Energy consumption in buildings, on the other hand, is influenced by various external factors, making it difficult to accurately predict through simulations. For instance, Mantovani et al. proposed a digital building model to simulate energy consumption [9]. Despite being highly consistent with the real building in terms of the floor plan, layout, and zone design, the accuracy of their simulation was impacted by changes in weather, season, and HVAC system conditions. ...
Preprint
With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). With HVAC systems accounting for about 40% of the total energy cost in the commercial sector, we propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices, which uses only a handful of controllable and accessible factors. The efficacy of our solution is evaluated through extensive analysis of the overall energy consumption and thermal comfort levels compared to a baseline system based on the existing HVAC schedule in a real building. This comparison shows that our method achieves 37% savings in energy consumption with minimum violation (<1%) of the desired temperature range during work hours. It takes only a total of 40 minutes for 5 epochs (about 7.75 minutes per epoch) to train a network with superior performance and covering diverse conditions for its low-complexity architecture; therefore, it easily adapts to changes in the building setups, weather conditions, occupancy rate, etc. Moreover, by enforcing smoothness on the control strategy, we suppress the frequent and unpleasant on/off transitions on HVAC units to avoid occupant discomfort and potential damage to the system. The generalizability of our model is verified by applying it to different building models and under various weather conditions.
... The linear representation of the system dynamic can be obtained either through classic linearization techniques (Rastegarpour et al., 2020a) or developing first principle linear models (Rastegarpour et al., 2018). The latter includes many strong assumptions in order to separate the linear part of the model from the nonlinear one, tuning the MPC on the linear process model only (Mantovani and Ferrarini, 2015). ...
... The problem of performance improvement and energy consumption reduction of the HVAC system in large scale buildings are also studied in (Mantovani and Ferrarini, 2015) for FIGURE 9 | Control architecture. MPC computes a power value to be delivered to each zone. ...
Article
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This paper presents a comparative analysis of different modeling and control techniques that can be used to tackle the energy efficiency and management problems in buildings. Multiple resources are considered, from generation to storage, distribution and delivery. In particular, it is shown what are the real needs and advantages in adopting different techniques, based on different applications, type of buildings, boundary conditions. This contribution is based widely on the experience performed by the authors in the recent years in dealing with existing residential, commercial and tertiary filed buildings, with application ranging from local temperature control up to smart grids where buildings are seen as an active node of the grid thanks to their ability to shape the thermal and electrical profile in real time. As for control models, a wide range of modeling techniques are here investigated and compared, from linear time-invariant models, to time-varying, to nonlinear ones. Similarly, control techniques include adaptive ones and real-time predictive ones.
... According to (42), we have ...
... To simulate the building thermal dynamics, the following model F is adopted similar to many existing works [40], [41], i.e., β in,i,t+1 = ε hvac β in,i,t + (1 − ε hvac )(β out,t − P sp,i,t η hvac /A i ). Note that the above model structure is not used for energy planning/optimization similar to model-based methods (e.g., model predictive control [42], and Lyapunov optimization techniques [43]), but used to obtain environment data for model-free learning. In addition, the adoption of the abovementioned model can facilitate the performance comparison with an optimal scheme that solves a deterministic model with perfect information of uncertain parameters. ...
Preprint
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In recent years, hydrogen-based multi-energy systems (HMESs) have received wide attention. However, existing works on the optimal operation of HMESs neglect building thermal dynamics, which means that the flexibility of thermal loads can not be utilized for reducing system operation cost. In this paper, we investigate an optimal operation problem of an HMES with the consideration of building thermal dynamics. Specifically, we first formulate an expected operational cost minimization problem related to an HMES. Due to the existence of uncertain parameters, inexplicit building thermal dynamics models, spatially and temporally coupled operational constraints, and nonlinear constraints, it is challenging to solve the formulated problem. Then, we propose an algorithm to solve the problem based on model-based optimization and data-driven based learning. The key idea of the proposed algorithm is summarized as follows: (1) transforming the long-term cost minimization problem into several single-slot subproblems using Lyapunov optimization techniques; (2) dividing each single-slot subproblem into two parts according to the availability of model information; (3) solving one part based on convex optimization and solving another part using multi-agent attention-based deep deterministic policy gradient. Simulation results based on real-world traces show the effectiveness of the proposed algorithm.
... Internet-based services also are suggested to solve the uncertainty problem in smart homes energy management studies. In [32], an aggregated model for all smart home components is proposed to deal with all uncertainties in loads, tariff, and PV power by the famous branch and bound technique with the help of a cloud service provider. The cloud service provider connects updated users' preferences, power market conditions, and metrological data as a cloud component. ...
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Solar-powered homes can be an optimal solution for the lack of continuous power sources problem in initial low-income communities. However, the challenge of Photovoltaic (PV) uncertainty can make it difficult to coordinate this vital solar energy in real-time. This paper proposes a new, low-cost solution for assessing the uncertainty of photovoltaic power generation in smart home energy management systems. The proposed index, inspired by the well-known clearness index, is an adaptive deterministic indicator that only requires free Geographic Information System (GIS) models and PV power measurement, without the need for expensive high-tech controllers or expert engineers/programmers. The proposed index successfully predicts the daily PV energy with errors of less than 3% for more than 93% of studied days, according to the 2020 measured solar radiation of the studied case in an African developing location, i.e. Cairo. Egypt.
... Since not all states are measurable in reality, a Kalman filter was used to estimate unmeasurable states and filter the noises in each cycle. Leveraging a dualpronged approach, which involves updating measurements and error covariance simultaneously, the Kalman filter has the capacity to estimate the current state at each starting point of the optimization process (Mirzaee and Salahshoor 2012;Maasoumy et al. 2014;Mantovani and Ferrarini 2015;Hu et al. 2019). ...
Article
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The presence or absence of occupants in a building has a direct effect on its energy use, as it influences the operation of various building energy systems. Buildings with high occupancy variability, such as universities, where fluctuations occur throughout the day and across the year, can pose challenges in developing control strategies that aim to balance comfort and energy efficiency. This situation becomes even more complex when such buildings are integrated with renewable energy technologies, due to the inherently intermittent nature of these energy source. To promote widespread integration of renewable energy sources in such buildings, the adoption of advanced control strategies such as model predictive control (MPC) is imperative. However, the variable nature of occupancy patterns must be considered in its design. In response to this, the present study evaluates a price responsive MPC strategy for a solar thermal heating system integrated with thermal energy storage (TES) for buildings with high occupancy variability. The coupled system supplies the building heating through a low temperature underfloor heating system. A case study University building in Nottingham, UK was employed for evaluating the feasibility of the proposed heating system controlled by MPC strategy. The MPC controller aims to optimize the solar heating system’s operation by dynamically adjusting to forecasted weather, occupancy, and solar availability, balancing indoor comfort with energy efficiency. By effectively integrating with thermal energy storage, it maximizes solar energy utilization, reducing reliance on non-renewable sources and ultimately lowering energy costs. The developed model has undergone verification and validation process, utilizing both numerical simulations and experimental data. The result shows that the solar hot water system provided 63% heating energy in total for the case study classroom and saved more than half of the electricity cost compared with that of the original building heating system. The electricity cost saving has been confirmed resulting from the energy shifting from high price periods to medium to low price periods through both active and passive heating energy storages.
... Moreover, many studies propose complicated deep learning models [46-48] to cover more weather scenarios' PV power behavior. Model predictive control techniques are usually suggested for smart home energy management systems to take into consideration many factors in the training process, such as tariff, temperature, ...etc [50][51]. Mis-training of such techniques may lead to unacceptable high error levels that range between 15 to 40%, as discussed in [49]. ...
Preprint
Full-text available
Solar-powered homes can be an optimal solution for the lack of continuous power sources problem in initial low-income communities. However, the challenge of PV uncertainty can make it difficult to coordinate this vital solar energy in real-time. This paper proposes a new, low-cost solution for assessing the uncertainty of photovoltaic power generation in smart home energy management systems. The proposed index, inspired by the well-known clearness index, is an adaptive deterministic indicator that only requires free Geographic Information System GIS models and PV power measurement, without the need for expensive high-tech controllers or expert engineers/programmers. The proposed index successfully predicts the daily PV energy with errors of less than 3% for more than 93% of studied days, according to the 2020 measured solar radiation of the studied case.
... In [35], a meta-zone aggregating multiple zones in real building was proposed to reduce the reliance on a high-resolution zone climate model of MPC. The strong nonlinear behavior is inevitable during the heat and moisture exchange between outdoor environment, ambient zones, and cannot be modeled appropriately [36]. Other uncertain or influential factors like random occupancy, outside weather and so on also impede precise mechanism modeling. ...
... In [19], the traffic control problem on highways is considered, but the centralized MPC's calculation time scales poorly. Due to the limitations of collecting all vehicle information and the challenges of calculation complexity of the optimization problems, the centralized MPC is not suitable for an actual vehicle platoon system [20], [21], [22]. However, the majority of the applications are used in a centralized approach, which means the controllers are designed by assuming that all of the states in the system are known. ...
Article
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In this paper, the string stable platoon control problem of discrete-time networked vehicle systems is considered by using distributed model predictive control (MPC) based method. An optimization problem is established to minimize the cost function associated to the system trajectories. The last-step shifting method is applied to set the local optimal solution as the assumed solution and send it to the neighbor vehicles. By using the sum of the cost function as Lyapunov function, the stability of the closed-loop platoon system is studied. Comparing with existing results, the string stability, which is the unique characteristics of the platoon system, is guaranteed under the bidirectional-based structure as well as the predecessor-follower-based information flow structure. Finally, several simulations are presented to demonstrate the effectiveness of the proposed algorithms.
... This verifies the close relationship between energy consumption in a building and thermal comfort. If the latter is optimized and neutrality is achieved with lower energy consumption, energy efficiency [28][29][30] is achieved. The work developed by Fanger [31] proposes the calculation of thermal comfort considering the following indoor variables: (i) level of physical activity, (ii) insulation level of the clothing, (iii) mean radiant temperature, (iv) Relative Humidity (RH), (v) temperature, and (vi) air velocity. ...
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There is a need to ensure comfortable conditions for hospital staff and patients from the point of view of thermal comfort and air quality so that they do not affect their performance. We consider the need for hospital employees and patients to enjoy conditions of greater well-being during their stay. This is understood as a comfortable thermal sensation and adequate air quality, depending on the task they are performing. The contribution of this article is the formulation of the fundamentals of a system and platform for monitoring thermal comfort and Indoor Air Quality (IAQ) in hospitals, based on an Internet of Things platform composed of a low-cost sensor node network that is capable of measuring critical variables such as humidity, temperature, and Carbon Dioxide (CO2). As part of the platform, a multidimensional data model with an On-Line Analytical Processing (OLAP) approach is presented that offers query flexibility, data volume reduction, as well as a significant reduction in query response times. The experimental results confirm the suitability of the platform’s data model, which facilitates operational and strategic decision making in complex hospitals.
... In (12), is the time horizon considered in this paper and E denotes the expectation operator, which acts on uncertain parameters, e.g., , out, , and . Although the above problem can be solved by stochastic programming and model predictive control [36], some prior knowledge should be required, e.g., probability distribution or prediction values. Typically, Lyapunov optimization techniques [17] can be used to design real-time algorithms for a decision-making problem under uncertainty. ...
Article
In a shared office space, the percentage of occupants with satisfied thermal comfort is typically low. The main reason is that heating, ventilation, and air conditioning (HVAC) systems can not provide individual thermal environment for each occupant within the shared office space. Although personal comfort systems (PCSs) can be adopted to implement heterogeneous thermal environments, they have limited adjustment abilities. At this time, coordinating the operations of PCSs and an HVAC system is a good choice. In this paper, the coordination control problem of PCSs and an HVAC system in a shared office space is investigated to minimize the total energy consumption while maintaining comfortable individual thermal environment for each occupant. Specifically, we first formulate an expected energy consumption minimization problem related to PCSs and an HVAC system. Due to the existence of an inexplicit building thermal dynamics model and uncertain parameters, it is challenging to solve the problem. To overcome the challenge, we reformulate the problem as a Markov game with heterogeneous agents. To promote an efficient cooperation of such agents, we propose a real-time control algorithm based on attention-based multi-agent deep reinforcement learning, which does not require an explicit building thermal dynamics model and any prior knowledge of uncertain parameters. Simulation results based on real-world traces show that the proposed algorithm can reduce energy consumption by 0.7%–4.18% and reduce average thermal comfort deviation by 64.13%–72.08% simultaneously compared with baselines.
... To improve overall performance of buildings there 24 1.1. Background is a need of systems that can be used to control multi-objectives of the buildings [11][12][13]. A Building Automation System (BAS), or a Building Automation Control System (BACS), is an intelligent controller installed in building to provide better understanding of required comfort conditions and occupants behaviors and controlling the HVAC systems optimally to avoid unwanted heating/cooling or any other operation based on the habits and behavior. ...
Thesis
Low cost smart sensors, intelligent controllers, and IoT systems constitute key components to develop smart buildings. These smart systems produce optimal control strategies by continuous analysis of building performance. Two major parameters are controlled in the building: occupants’ comfort and heating or cooling load consumption optimization. For such intelligent controllers applications, it is essential to have building model with high performance accuracy and computational efficiency. The existing building models range from complete analytical to fully data-driven and hybrid models. The analytical model is extremely complex to model and computationally inefficient, whereas the data-driven models require a large amount of data. However, in the case of data unavailability, application of datadriven models become impossible. This work presents, hybrid modeling for heat transfer dynamics of the building using lumped parameter thermal network modeling technique. An efficient building model is developed by having proper structural knowledge of low-order model and identifying its parameter values. Simplified low-order systems are developed using 2nd order thermal network models with optimal thermal resistors and capacitors value.In order to determine the low-order model parameter values, a specific approach is proposed using a stochastic particle swarm optimization. This method provides a significant approximation of the parameters when compared to the reference model whilst allowing low-order model to achieve 40% to 50% computational efficiency than the reference analytical model.Furthermore, extensive simulations are carried out to evaluate the proposed simplified model with a more advanced complex solar gains model and identified parameters value. The developed simplified model is afterward validated with measured data from a case study building where the achieved results clearly show a high degree of accuracy compared to the actual data. Finally, an MPC controller is applied for the same case study building for thermal comfort optimization. Simulation results demonstrate the significance of the MPC controller in handling the constraints, multi-objective control, and producing optimal control strategy. The energy optimization results of the MPC have shown 31% of energy consumption reduction compared to a conventional controller.
... Traditionally, the MGs' energy management issue is formulated as a sequential optimization problem that determines appropriate set points for the controllable devices so that energy costs are minimized. Several optimization frameworks have been proposed in [12]- [15], including mixed-integer linear optimization (MILP) in [12], nonlinear optimization in [13], and model predictive control (MPC) in [14] and [15], which target to minimize the operating cost of buildings' HVAC systems, while also considering the occupants' thermal comfort. MPC and MILP frameworks have been also applied in [16] and [17], respectively, for the energy cost optimization of smart homes that contain RES, ESSs, and controllable loads. ...
Article
Smart homes that contain renewable energy sources, storage systems, and controllable loads will be key components of the future smart grid. In this article, we develop a reinforcement-learning (RL)-based scheme for the real-time energy management of a smart home that contains a photovoltaic system, a storage device, and a heating, ventilation, and air conditioning (HVAC) system. The objective of the proposed scheme is to minimize the smart home’s electricity cost and the residents’ thermal discomfort by appropriately scheduling the storage device and the HVAC system on a daily basis. The problem is formulated as a Markov decision process, which is solved using the deep deterministic policy gradient (DDPG) algorithm. The main contribution of our study compared to the existing literature on RL-based energy management is the development of a clustering process that partitions the training data set into more homogeneous training subsets. Different DDPG agents are trained based on the data included in the derived subsets, while in real time, the test days are assigned to the appropriate agent, which is able to achieve more efficient energy schedules when compared to a single DDPG agent that is trained based on a unified training data set.
... Luo et al. proposed a novel distance control strategy to solve the multi-objective coordinate control problem by using objects decoupling control; the proposed control system is beneficial to real-time performance [14,15]. For multi-objective optimization, MPC is widely used for its effectiveness and optimal performance [16,17]. However, heavy computation burden is a problem for numerical optimization [18]. ...
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Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.
... In [20], the authors present an MPC strategy for a building cooling system equipped with thermal energy storage. In [21], in order to reduce the vertical temperature stratification in multi-floor commercial buildings, MPC is applied to control the specific floor temperature. In [22], a transactive control approach is presented for commercial building HVAC system demand response. ...
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Chiller-air handing units (Chiller-AHU) system is widely used in the heating, ventilation and air conditioning (HVAC) system of commercial and residential buildings. An effective control strategy is critical for the Chiller-AHU plant to reduce energy consumption and costs. Past control strategies focus more on leveraging building load flexibility to decrease the cost bill by setting the different room temperatures according to different electricity prices. Neglecting how to achieve the set room temperature with minimized total power consumption. In addition, the Chiller-AHU system is not considered as a whole and the constant coefficient of performance (COP) of the chiller is assumed in some research. All these limitations cause compromised results. In this paper, we build a complete Chiller-AHU model and propose a nonlinear model predictive control (MPC) strategy for the Chiller-AHU system. The MPC can provide the optimal inputs for the system, the minimal total power consumption can be achieved by optimally coordinating the operating from fans, pumps and chiller providing that the room temperature is well maintained. The proposed strategy is evaluated under the energy-saving case and cost-reduction case, a 6.2% total power consumption saving and 12.3% electricity bill reduction is achieved compared to the traditional PI control. The simulation results validate the effectiveness of the proposed strategy.
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Improving human health and comfort in buildings requires efficient temperature regulation. Temperature control system has a significant contribution in minimizing the impact of climate change. Temperature control system is used in industry to control temperature. The polar form of complex Pythagorean fuzzy set is a limited notion because when decision makers take the value for membership degree as 0.71+ι0.81 then we can observe that the basic condition for complex Pythagorean fuzzy set fails to hold that is r=0.712+0.812=1.3661∉[0,1]. Moreover, we can observe that the Cartesian form of a complex Pythagorean fuzzy set is also a limited notion because it can never discus advance data. Hence keeping in mind these limitations of the existing notions, in this article, we have explored the Cartesian form of a complex q-rung orthopair fuzzy set. Moreover, we have developed the Yager operational laws based on a Cartesian form of complex q-rung orthopair fuzzy set. We have introduced aggregation theory named complex q-rung orthopair fuzzy Yager weighted average and complex q-rung orthopair fuzzy Yager weighted geometric aggregation operators in Cartesian form. Based on these aggregation operators, we have initiated a multi-attribute group decision-making (MAGDM) approach to define the reliability and authenticity of the developed theory. Furthermore, we have utilized this device algorithm in the selection of a temperature control system. The comparative study of the delivered approach shows the advancement and superiority of the delivered approach.
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The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.
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The present paper shows how the dynamic indoor temperature profile of an HVAC (Heating, Ventilation, and Air Conditioning) system in a building can be developed using Kalman filters, in presence of unknown inputs. An RC network based dynamic, nonlinear thermal model is first developed for the indoor environment with a novel consideration of relative humidity factor. Then an extended Kalman Filter based algorithm in presence of unknown inputs (called EKF-UI ) and an adaptive variation of this EKF-UI algorithm (called AdEKF-UI ) are developed for the real indoor environment under consideration. Next, a particle swarm optimization ( PSO ) guided adaptive extended Kalman filter with unknown inputs ( PSOgAdEKF-UI ) algorithm is proposed to overcome limitations of the EKF-UI and AdEKF-UI algorithms, especially under bad initialization situations. This PSOgAdEKF-UI algorithm proposes an effective utilization of regularizer based initializations for the initial state estimation error covariance matrix and the measurement noise covariance matrix. Extensive experiments showed that, overall, PSOgAdEKF-UI algorithm could outperform EKF-UI and AdEKF-UI algorithms by 46.59% and 20.66%, respectively, in terms of mean square error, while estimating an unknown state. Note to Practitioners —This paper was motivated to estimate the nonlinear dynamics of indoor HVAC thermal profile in presence of unknown inputs. The study explores a proposed Kalman filter-based heuristic regularizer-assisted adaptive filtering methodology for nonlinear state estimation that can circumvent the constraints imposed by current approaches. The proposed method demonstrates its applicability in actual nonlinear physical systems since many matrices needed for such state estimation algorithms do not have accurate initialization information. The nature of inferential stochastic inputs in practical HVAC system can be evaluated utilizing our novel state estimation method of the altering relative humidity coupled nonlinear dynamic thermal model. The thermal profile of a practical HVAC system, in presence of varying unknown inputs, can be more accurately modeled when temporal variations in relative humidity are included in the nonlinear dynamic model, as an additional influencing factor.
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Managing Heating, Ventilation and Air-Conditioning (HVAC) optimally with respect to energy efficiency and adaptively with respect to uncertainties is a recognized bottleneck for multi-zone buildings. This work proposes a management approach where HVAC operation is approximated using averaged thermal dynamics. By doing this, energy management becomes effective due to the linear form of the averaged dynamics, making it possible to use optimal and adaptive control tools. The proposed approach exhibits favorable features for implementation in energy management programs: minimal parameters to be estimated (only those directly related to the thermal coefficients); preservation of the distributed multi-zone topology (only the thermal coefficients with neighboring rooms need to be estimated); simplicity of imposing positivity and controllability properties. Optimality and adaptation are studied analytically in the framework of adaptive linear quadratic control. Numerical validations show that the proposed approach strikes optimal trade-offs between energy consumption and thermal comfort, while handling unknown and possibly time-varying parameters. Improved trade-offs span 3-12% with respect to PID control, 14-47% with respect to non-adaptive control, and 1-20% with respect to alternative adaptation methods proposed in the literature.
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Implementing an efficient control strategy for heating, ventilation, and air conditioning (HVAC) systems can lead to improvements in both energy efficiency and thermal performance in buildings. As HVAC systems and buildings are complicated dynamic systems, the effectiveness of both data-driven and model-based control methods has been widely investigated by researchers. However, the main challenges that impede the practical application of model-based methods in real buildings are their reliance on the precision of control-oriented models and the dependence of data-based systems on the quantity and quality of input–output data. The objectives of this study are: (1) To present an overview of the prevalent thermal modelling strategies used as control-oriented models or virtual environments in model-based and data-based control methods, addressing the main requirements of thermal models; (2) the state-of-the-art of MPC and RL control techniques; (3) the data requirements for thermal models. The findings emphasise the need for unified guidelines to validate and verify the proposed control methods, ensuring their practical implementation in real buildings. Moreover, the inclusion of occupancy forecasts in models presents challenges due to the intricate nature of accurately predicting human behaviour, occupancy patterns, and their effects on thermal dynamics. Balancing thermal comfort and energy efficiency in HVAC systems with a supervisory controller remains a difficult task, but combining data-driven and physics-based models can help overcome challenges. Further research is needed to compare the effectiveness of MPC and RL approaches, and accurately measuring the impact of human behaviour and occupancy remains a significant obstacle.
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We present a distributed model predictive control method which enables a group of agents to compute their control inputs locally, while communicating with their neighbors over a communication network. While many distributed model predictive control methods require a central station for some coordination or computation of the optimization variables, our method does not require a central station, making our approach applicable to a variety of communication network topologies. With our method, each agent solves for its control inputs without solving for the control inputs of other agents, allowing for efficient optimization by each agent, unlike some other distributed methods. Further, our method attains linear convergence to the optimal control inputs in convex model predictive control problems, improving upon the sub-linear convergence rates provided by some other distributed methods such as dual decomposition methods. Moreover, our algorithm provides a closed-loop controller for convex model predictive control problems with affine constraints. We demonstrate our method in both convex and non-convex model predictive control problems in wireless transceiver alignment and satellite deployment, where we show robustness of our method to time delays.
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Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions.
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This paper proposes a novel data-driven robust model predictive control (MPC) framework for a multi-zone building considering thermal comfort and uncertain weather forecast errors. The control objective is to maintain each zone's temperature and relative humidity within the specified ranges by minimizing the energy usage of the underlying heating system. A state-space model is developed to use a hybrid physics-based and data-driven method for the multi-zone building's temperature and relative humidity. The temperature and humidity RMSEs between the state-space model and the EnergyPlus-based model are less than 0.25 °C and 5.9%, respectively. The uncertainty space is based on historical weather forecast error data, which are clustered by using a k-means clustering algorithm. Machine learning approaches, including principal component analysis and kernel density estimation, are used to construct each basic uncertainty set and reduce the conservatism of resulting robust control action under disturbances. A robust MPC framework is built upon the proposed state-space model and data-driven disjunctive uncertainty set. An affine disturbance feedback rule is employed to obtain a tractable approximation of the robust MPC problem. Besides, the feasibility and stability of the proposed MPC are discussed in detail. A case study of controlling temperature and relative humidity of a multi-zone building in Ithaca, New York, USA, is presented. The results demonstrate that the proposed framework can reduce up to 8.8% of total energy consumption compared to conventional robust MPC approaches. Moreover, the proposed framework can essentially satisfy the thermal constraints that certainty equivalent MPC and robust MPC largely violate.
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The conventional multi-zone HVAC (Heating Ventilation and Air Conditioning) system includes a lot of Variable Air Volume (VAV) boxes to regulate the corresponding zone temperature, and centralized equipment (air supply fans and ducts, etc.) to supply conditioned air in the air-side. With the limitation of the total supply air mass rate in centralized equipment and the priority of zones’ requirement, it becomes a question of resource allocation and schedule on how to coordinate air mass rate to each zone. It is also intricate to control each zone’s temperature in a fully distributed fashion due to this global constraint. In this paper, a distributed model predictive control (DMPC) with priority coordination is proposed for multi-zone HVAC systems. This strategy is composed of a priority coordinated layer and a lower DMPC layer. In the coordinated layer, a thermal bound calculation mechanism with priority is presented to adjust thermal bounds for DMPC to resolve the resource allocation problem when the subsystems are competing for the resource. In DMPC, an operational cost-saving controller is put forward, and the global resource limit is guaranteed by transforming the optimization problem into a consensus problem, which is running in a fully distributed way. In this control scheme, the original infeasible DMPC problem is relaxed, and the entire system is functioning well with specified performance compromise. The effectiveness of the proposed scheme is proved by simulation examples.
Conference Paper
Controlling Heating, Ventilation and Air-Conditioning (HVAC) in a way that is both optimal (energy efficient) and adaptive (able to cope with uncertainties) is a recognized challenge in the building sector. This work presents a simple modelling approach to HVAC operation aiming for adaptive and optimal control. The proposed framework exhibits several advantages amenable for implementation in energy management systems: minimum number of parameters to be estimated; preserving the distributed nature of multiple zones in the estimation process; easiness of imposing positivity and controllability constraints on the estimated coefficients. Optimality is addressed using an adaptive linear quadratic control philosophy. Comparisons with Proportional-Integral-Derivative (PID) and non-adaptive controllers show the capability of the proposed framework of coping with uncertainty while delivering nearly optimal controls.
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Model Predictive Control (MPC) has gained popularity in recent years and is widely adopted in building control. This study proposes a novel data-driven robust MPC to make the optimal heating plan, specifically for the multi-zone single-floor building. In this study, the room temperature and relative humidity (RH) will be highly valued in the optimization decision. To better incorporate RH into the state-space model (SSM), the linear relations between RH and other room temperature parameters in the thermal zones are formulated, ensuring the better linear fitting of SSM to the original nonlinear model. Afterward, k-means clustered, principal component analysis (PCA), and kernel density estimation (KDE) based data-driven uncertainty set is constructed and applied to MPC. The other three kinds of MPC’s are compared to our proposed data-driven robust MPC (RMPC), including conventional RMPC, k-means clustered, data-driven RMPC, PCA and KDE based data-driven RMPC. The results demonstrate that the optimality of our proposed k-means clustered, PCA and KDE based data-driven RMPC, which consumes 9.8 % to 17.9 % less energy in controlling both temperature and RH, compared to other data-driven robust MPC’s, and essentially follow the constraints which certainty equivalent MPC and conventional RMPC cannot conform.
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The environment with high-stability temperature is crucial for precision machinery such as lithography and laser interferometer. Some studies have investigated temperature control algorithms and temperature fluctuation attenuators to provide precision temperature control systems for them. In this paper, considering the characteristics of hardware elements and the closed control loop overall, a general control loop connected by multiple cascade loops is proposed for high-precision air temperature control to reject the disturbance from the inlet air. It is found that this disturbance and the noise generated by the non-uniform air temperature of the chamber are the two main sources affecting the control precision. Based on loop shaping, a controller with compensators is designed for the cascade control loop to make a tradeoff between the disturbance rejection and noise attenuation. The lumped parameter theoretical models of three kinds of attenuators for air temperature fluctuation are analysed for the design of precision air temperature control. An air temperature control system is used to verify the performance of the proposed controller. The results demonstrate that the controller can improve the disturbance rejection performance while reducing the sensitivity to the noise. The 1σ derivation of the chamber air temperature is decreased from 15.6mK to 10.5mK.
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We formulate a model predictive control (MPC) for linear time-invariant systems based on H-infinity loop-shaping. The design results in a closed-loop system that includes a state estimator and attains an optimized stability margin. Input and output weights are designed in the frequency domain to satisfy steady-state and transient performance requirements, in lieu of standard MPC plant model augmentations. The H-infinity loop-shaping synthesis results in an observer-based state feedback structure. An inverse optimal control problem is solved to construct the MPC cost function, so that the control input computed by MPC is equal to the H-infinity control input when the constraints are inactive. The MPC inherits the closed-loop performance and stability margin of the loop-shaped design when constraints are inactive. We apply the methodology to a multizone heat pump, and validate the results in simulations and laboratory experiments. The design rejects constant unmeasured disturbances, tracks constant references with zero steady-state error, meets transient performance requirements, provides an excellent stability margin, and enforces input and output constraints.
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The heating, ventilation, air conditioning (HVAC) system is a common ventilation system applied to an indoor environment based on Internet of Things (IoT) technology. In HVAC, providing a comfortable temperature under the constraint of energy is a major challenge. In this article, we propose an air quality optimization strategy to control the air supply and energy consumption, and build several dynamic models to capture the stochastic processes in HVAC. Besides, a system utility maximization problem is formulated. We provide a solution framework based on the Lyapunov optimization method. Based on this framework, we propose a utility-optimal air quality optimization algorithm to solve the subproblem, and theoretically prove that it can achieve the near-optimal system utility. Additionally, the upper bound of the indoor temperature is derived, and the optimality of the algorithm is analyzed. Simulation results show the impact of the system parameter on the HVAC system and the indoor temperature, and verify that the proposed strategy and methods can maintain the comfortable temperature range and supply more fresh air effectively under the constraints of energy consumption.
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Distributed control that aims for consensus tasks of multi-agent systems has progressed rapidly with a wide range of applications
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In this chapter, temperature control of a delay dependent, nonlinear air heater system is investigated. The results show that, the lbest HS-L1 adaptive controller tracks the reference temperature trajectory better than the basic L1 adaptive controller. The fuzzy PDC-L1 adaptive controller outperforms the other controllers by nullifying time varying uncertainties and disturbances utilizing fuzzy L1 adaptive controller and handling nonlinearities, delays with the help of fuzzy PDC controller.
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This paper investigates memory nonfragile mixed-objective output feedback robust model predictive control (OFRMPC) for a class of uncertain systems subjected to physical constraint, bounded disturbance, unmeasurable delayed state and possible controller fragility. By employing a delay-independent Lyapunov-Krasovskii function and linear matrix inequality (LMI) framework, novel sufficient conditions for the proposed memory nonfragile OFRMPC are derived to asymptomatically stabilize the closed-loop system with guaranteed H∞/H2 performance for all admissible polytopic uncertainties, external disturbance, state delay, and additive or multiplicative gain perturbation. A key technique for this controller is the online optimization of an infinite-horizon objective function followed by a memory output feedback control law based on the pre-specified offline state estimator using modified quadratic bounded conditions. Moreover, the input constraint and the recursive feasibility have been further guaranteed via additional LMI-based conditions. Finally, a numerical example is given to illustrate the effectiveness of the proposed OFRMPC approach.
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In this paper, a coordinated strategy of distributed model predictive control (DMPC) is proposed to accommodate Air Handling Unit (AHU) and Variable Air Volume boxes (VAV boxes) in a multi-zone HVAC (Heating Ventilation and Air Conditioning) system. A modified distributed model predictive control with equivalent local cooling cost and a total-air-mass-rate penalty term is implemented to local VAV boxes to regulate indoor temperature. The penalty term and AHU supply air temperature are updated in the upper coordinated layer to guarantee the total air mass rate within the limit and economic operation. In this framework, with the help of coordination from both DMPC and the upper layer, the operational cost from the HVAC system can be reduced. The effectiveness of the proposed scheme is proved by a numerical simulation.
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A ventilation method of down-supply up-return has grown popular in large space in recent years for comfort ventilation with low thermal load, especially in China, including gymnasium, factory space, and exhibition hall, etc. The undisturbed flow pattern in the space gives a gradient in temperature, and the vertical thermal stratification appears markedly in large space. The object for this paper is to understand the behaviour of an under floor air distribution system in a ventilated space. The thermal stratification characteristics in a real UFAD experimental space were measured. The effects of different supply conditions on the thermal stratifi-cation characteristics are investigated based on the experimental results. The relations between space air stra-tification and the control parameter is predicted. It can be indicated that there are 4 zones composing the vertical thermal stratification. And different zone has different control parameter.
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This article presents an analysis of the relationship between building energy usage and building control system operation and performance. A method is presented for estimating the energy saving potential of improvements in building and control system operation, including the relative impact of recommssioning and hardware and software upgrades, based on a subjective assessment of the level of energy efficient design and the energy usage of the building relative to similar buildings as indicated by the Energy Utilization Index for the building. The method introduces a Building Design Index and a Building Operating Index to evaluate building energy performance versus similar buildings, and uses these indices to estimate potential savings and effectiveness of control system improvements.
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We propose three different Model Predictive Control (MPC) strategies for controlling temperatures in buildings. We show that maximization of thermal comfort, along with minimization of energy consumption, can be cast in various ways, each having their pros and cons. The three strategies include tracking of setpoint temperatures, tracking of a comfort zone, and minimization of number of violations of such a zone. Even though the latter formulation is the most demanding from a computational point of view, it indeed provides best thermal comfort. All proposed methods are compared with respect to two qualitative criteria on simulations.
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a b s t r a c t This paper presents a physics-based autogregressive moving average (ARMAX) model of room temper-ature in office buildings. Thermodynamic equations are used to determine the structure and order of the model. Extensive measurements over 109 days are used to develop and validate the model. The model can be used to predict the room temperature variations accurately in both short-term and long-term periods, as long as ten weeks with a mean squared error less than 0.01 and a coefficient of determi-nation larger than 0.99. This model also provides an analytical foundation of the previously proposed spatial and temporal partition strategy of VAVs for fault detection of HVAC systems, and is suitable for real-time fault detection and control applications.
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One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since buildings account for about 40% of global final energy use, energy efficient building climate control can have an important contribution. In this paper we develop and analyze a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account weather predictions to increase energy efficiency while respecting constraints resulting from desired occupant comfort. We investigate a bilinear model under stochastic uncertainty with probabilistic, time varying constraints. We report on the assessment of this control strategy in a large-scale simulation study where the control performance with different building variants and under different weather conditions is studied. For selected cases the SMPC approach is analyzed in detail and shown to significantly outperform current control practice.
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A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency.
Conference Paper
In the paper, the modeling, control and energy management of a large-commercial building is presented. This study is finalized to improving the building energy performances through an extensive use of automation and control strategies. Shown results demonstrate the effectiveness of such techniques, leading to the research of further solutions to decrease energy consumptions in large buildings.
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The building sector is the largest energy consumer in the world. Therefore, it is economically, socially, and environmentally significant to reduce the energy consumption of buildings. Achieving substantial energy reduction in buildings may require rethinking the whole processes of design, construction, and operation of a building. This article focuses on the specific issue of advanced control system design for energy efficient buildings.
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A predictive current controller with an extended-state observer (ESO) is proposed for grid integration of wind energy systems. In each sampling period, the proposed strategy calculates the converter switching time that minimizes a cost function defined as a sum of squared current errors, leading to constant switching frequency. To achieve excellent dynamic performance, the impact of sampling delay is analyzed, and detailed compensation methods are proposed. In addition, an ESO is constructed to suppress parameter variations and modeling errors, which affect the performance of the controller. The parameter tuning and the stability of the observer are analyzed. The proposed strategy not only presents rapid dynamic response due to the use of the predictive current controller but also possesses robust control performance as a result of the observation algorithm. Simulation and experimental results are given to validate the effectiveness of the proposed solution.
Conference Paper
In order to reduce heat energy demand in residential building, thermal insulation and indoor air tightness become more important. However, in a well-insulated environment, internal heat gain caused by solar radiation, metabolism and losses of home electric appliance (i.e. refrigerator, lamp, television, etc.) can be dominant to home global energy management. To quantify and to modelize the heat gain due to home appliances, we begin experimental measurements in a well-insulated room. The first step in this work is the identification of the room. In this paper we suggest a 1R1C lumped parameter circuit which presents a building thermal model using thermal-electric analogy. Then, we identify the circuit components (the global thermal resistor and the global thermal capacitor) from the heat balance equation and experimental results. Based on the model and the obtained parameters, we simulate the indoor temperature of the model using Matlab/Simulink. To check its accuracy we compare the measured data and simulation results and calculate their error ratio.
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In order to reduce heat energy demand in residential building, thermal insulation and indoor air tightness become more important. However, in a well-insulated environment, internal heat gain caused by solar radiation, metabolism and losses of home electric appliance (i.e. refrigerator, lamp, television, etc.) can be dominant to home global energy management. To quantify and to modelize the heat gain due to home appliances, we begin experimental measurements in a well-insulated room. The first step in this work is the identification of the room. In this paper we suggest a 1R1C lumped parameter circuit which presents a building thermal model using thermal-electric analogy. Then, we identify the circuit components (the global thermal resistor and the global thermal capacitor) from the heat balance equation and experimental results. Based on the model and the obtained parameters, we simulate the indoor temperature of the model using Matlab/Simulink. To check its accuracy we compare the measured data and simulation results and calculate their error ratio.
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The goal of optimal temperature control in buildings is usually to ensure thermal comfort with minimal energy consumption. In intermittently occupied buildings, this presumes the ability of the controller to recover in due time the building from setback. Model Predictive Control (MPC) is considered among the best candidates for this task due to its ability to use occupancy schedule and weather forecasts for optimal temperature control. However, the use of the classical cost function within MPC does not allow to achieve the objectives of minimal energy consumption and optimal restart of the heating system. Therefore, a new cost function is introduced, which minimizes the energy consumption while maintaining the thermal comfort in the building. The obtained linear optimization problem is formulated to fit into the canonical form of Linear Programming method.
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An important performance in building thermal control is to ensure thermal comfort with minimal energy consumption. Model Predictive Control (MPC) is considered to be one of the most suited solutions for this due to its ability to use occupancy schedule and weather forecasts for optimal temperature control. MPC relies on a dynamical model of the building, which is the main difficulty of applying it. Therefore, this paper treats the problems related to building modeling and model parameters identification. A robust model of the building is obtained in two stages: firstly physical knowledge is used to determine the structure of a low-order model, then least squares identification method is applied to find the numerical values of the model parameters. In order to perform the identification usually there are required input/output data records having variations which generally are not accepted in inhabited buildings because of imposed comfort conditions. Also inhabited buildings contain unmeasured disturbance sources which may degrade the identified model quality. Therefore this paper proposes to use detailed building models, implemented in dedicated simulation tools, to generate the required input/output data records instead of measuring them on real buildings. This allows us to apply desired input signals and to eliminate disturbance sources. Additionally, the paper presents a method to identify the nonlinearity existing in building thermal behavior, which permits to represent the building by separated linear and nonlinear blocks. This model representation, used along with the linearization method proposed in Part II, permits to design the temperature controller without resorting to the nonlinear system theory.
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Constructing a model of thermal dynamics of a multi-zone building requires modeling heat conduction through walls as well as convection due to air-flows among the zones. Reduced order models of conduction in terms of RC-networks are well established, while currently the only way to model convection is through CFD (Computational Fluid Dynamics). This limits convection models to a single zone or a small number of zones in a building. In this paper we present a novel method of identifying a reduced order thermal model of a multi-zone building from measured space temperature data. The method consists of first identifying the underlying network structure, in particular, the paths of convective interaction among zones, which corresponds to edges of a building graph. Convective interaction among a pair of zones is modeled as a RC network, in a manner analogous to conduction models. The second step of the proposed method involves estimating the parameters of the RC network model for the convection edges. The identified convection edges, along with the associated R and C values, are used to augment a thermal dynamics model of a building that is originally constructed to model only conduction. Predictions by the augmented model and the conduction-only model are compared with space temperatures measured in a multi-zone building in the University of Florida campus. The identified model is seen to predict the temperatures more accurately than a conduction-only model.
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A computationally-efficient building thermal model is developed for short-timescale investigations applicable to control system design. A lumped- capacity treatment of the building elements is used which can be summarised by a simple analogue electric circuit. The method is procedurally- transparent and leads to a state-space description of a building space which is implemented using MATLAB/SIMULINK. The model is tested using experimental data from a building with high thermal capacity. The accuracy of second-order descriptions of the high thermal capacity building elements compared with first-order descriptions is also examined. Good agreement with experimental data over short simulation periods is obtained using first- order element descriptions.
Conference Paper
Model Predictive Control (MPC) can be used to control a system of energy producers and consumers in a Smart Grid. In this paper, we use heat pumps for heating residential buildings with a floor heating system. We use the thermal capacity of the building to shift the energy consumption to periods with low electricity prices. In this way the heating system of the house becomes a flexible power consumer in the Smart Grid. This scenario is relevant for systems with a significant share of stochastic energy producers, e.g. wind turbines, where the ability to shift power consumption according to production is crucial. We present a model for a house with a ground source based heat pump used for supplying thermal energy to a water based floor heating system. The model is a linear state space model and the resulting controller is an Economic MPC formulated as a linear program. The model includes forecasts of both weather and electricity price. Simulation studies demonstrate the capabilities of the proposed model and algorithm. Compared to traditional operation of heat pumps with constant electricity prices, the optimized operating strategy saves 25–35% of the electricity cost.
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In this paper, a model predictive control (MPC) design method is proposed for Hammerstein systems with multivariable nonlinearities. Iterative inversion is introduced such that classical linear MPC design can be used. The condition for which the numerical inversion is guaranteed to converge is derived. From this condition, the input space at which the nonlinear function is invertible can be obtained, which poses extra input constraints for MPC design. The overall constraints are transformed and approximated by a set of linear constraints by means of convex hull construction. Illustrative examples are presented to demonstrate the effectiveness of the proposed control method.
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Methods of implementing an input-constrained, nonlinear, model-predictive controller in latent spaces using partial-least-squares (PLS)-based Hammerstein and Wiener models are discussed. For multiple-input, multiple-output (MIMO) systems, the PLS flamework presents a viable alternative for identification and controller synthesis using Hammerstein and Wiener structures. The constraint mappings, which have to be taken into account during controller design in the PLS flamework, are highlighted. PLS-based Wiener models are well suited for constrained control of nonlinear systems. The use of PLS-based Hammerstein models for control involves solution of a nonlinear program as a result of the constraint mapping. The proposed approach is demonstrated on a simulated pH-level control of an acid base neutralization process.
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In this paper, we propose an intelligent data-analysis method for modeling and prediction of daily electricity consumption in buildings. The objective is to enable a building-management system to be used for forecasting and detection of abnormal energy use. First, an outlier-detection method is proposed to identify abnormally high or low energy use in a building. Then a canonical variate analysis is employed to describe latent variables of daily electricity-consumption profiles, which can be used to group the data sets into different clusters. Finally, a simple classifier is used to predict the daily electricity-consumption profiles. A case study, based on a mixed-use environment, was studied. The results demonstrate that the method proposed in this paper can be used in conjunction with a building-management system to identify abnormal utility consumption and notify building operators in real time.
Conference Paper
We propose a method for model-reduction of a class of non-linear models that are relevant to modeling thermal dynamics of multi-zone buildings. These models can have large state-space dimension even for a moderate number of zones. Reduced order models of building thermal dynamics can be useful to model-based control for improving energy efficiency, especially to computationally intensive ones such as Model Predictive Control (MPC). Although there are a number of well-developed techniques for model reduction of LTI systems, the same cannot be said about non-linear systems. The method we propose exploits the linear portion of the model to compute a transformation (by using balanced realization) and a specific sparsity pattern of the non-linear portion to obtain the reduced order model. Simulations are presented with a four zone building model, which show that the prediction of the zone temperatures and humidity ratios by the reduced model is quite close to that from the full-scale model, even when substantial reduction of model order is specified.
Conference Paper
The objective of this study is to demonstrate the effectiveness of model predictive control (MPC) in reducing the energy and demand costs for buildings in an electricity grid with time-of-use pricing and demand charges. A virtual model for a single floor, multi-zone commercial building equipped with a variable air volume (VAV) cooling system is built by Energyplus. Real-time data exchange between Energyplus and Matlab controller is realized by introducing the building controls virtual test bed (BCVTB) as a middleware. System identification technique is implemented to obtain the zone temperature and power model, which are to be used in the MPC framework. MPC with an economic objective function is formulated as a linear programming problem and solved. Pre-cooling effect during off-peak period and autonomous cooling discharging from the building thermal mass during on-peak period can be observed in a continuous weekly simulation. Cost savings brought by MPC are given by comparing with the baseline and other pre-programmed control strategies.
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The rapidly growing world energy use has already raised concerns over supply difficulties, exhaustion of energy resources and heavy environmental impacts (ozone layer depletion, global warming, climate change, etc.). The global contribution from buildings towards energy consumption, both residential and commercial, has steadily increased reaching figures between 20% and 40% in developed countries, and has exceeded the other major sectors: industrial and transportation. Growth in population, increasing demand for building services and comfort levels, together with the rise in time spent inside buildings, assure the upward trend in energy demand will continue in the future. For this reason, energy efficiency in buildings is today a prime objective for energy policy at regional, national and international levels. Among building services, the growth in HVAC systems energy use is particularly significant (50% of building consumption and 20% of total consumption in the USA). This paper analyses available information concerning energy consumption in buildings, and particularly related to HVAC systems. Many questions arise: Is the necessary information available? Which are the main building types? What end uses should be considered in the breakdown? Comparisons between different countries are presented specially for commercial buildings. The case of offices is analysed in deeper detail.
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A building is permanently in thermodynamic non-equilibrium due to changing weather, free gains and indoor temperature set-point. Load calculation in dynamic conditions is an essential goal of building energy simulation. This paper demonstrates that the load calculation is a control problem. Supposing that the thermal model of the building is linear and that the model of the building, the weather conditions and occupational program are known in the design stage, the paper proposes an unconstrained optimal control algorithm which uses feed-forward to compensate the weather conditions and model predictive programming (MPP) for set-point tracking. MPP is obtained by modifying the dynamic matrix control (DMC), a variant of model predictive control (MPC).The peak load depends on the set-back time of the indoor temperature: smaller the set-back time, larger the peak load, but smaller energy consumption. Then, the choice of the weighting coefficients in the model predictive programming may be done on economical considerations.
Article
The present work is focused on the study of indoor thermal comfort control problem in buildings equipped with HVAC (heating, ventilation and air conditioning) systems. The occupants’ thermal comfort sensation is addressed here by the well-known comfort index known as PMV (predicted mean vote) and by a comfort zone defined in a psychrometric chart. In this context, different strategies for the control algorithms are proposed by using an only-one-actuator system that can be associated to a cooling and/or heating system. The first set of strategies is related to the thermal comfort optimization and the second one includes energy consumption minimization, while maintaining the indoor thermal comfort criterion at an adequate level. The methods are based on the model predictive control scheme and simulation results are presented for two case studies. The results validate the proposed methodology in terms of both thermal comfort and energy savings.
Use of weather and occupancy forecasts for optimal building climate control (opticontrol): Two years progress report
  • D Gyalistras
  • M Gwerder
Model predictive control for the operation of building cooling systems
  • Y Ma
Indoor thermal environment and vertical temperature gradient in large workshop of school without air-conditioning
  • S Yasui
Model predictive control of Hammerstein systems with multivariable nonlinearities
  • ho