Control Of Heating, Ventilation And Air Conditioning Systems Research Articles

Development of an indoor environment evaluation model for heating, ventilation and air-conditioning control system of office buildings in subtropical region considering indoor health and thermal comfort

Office occupants spend most of their time in an enclosed indoor environment, controlled by heating, ventilation and air-conditioning (HVAC) systems especially in subtropical regions owing to the hot and humid climate. A reasonable indoor environment evaluation model is necessary to achieve the reliable control of HVAC systems that satisfies the occupants’ health and comfort needs. However, traditional HVAC systems are controlled based on a simple index that does not consider the synthesis of indoor air quality, thermal comfort and occupant preferences. In this paper, we develop a comprehensive evaluation model that encompasses these three aspects based on field survey. Field surveys were conducted to investigate indoor environmental conditions and preferences of the occupants. Collected data were then verified for model hypothesis rationality and reviewed to identify weighting factors using Pearson and regression analysis. Results showed that these parameters had significant correlations without noticeable collinearity and can be integrated using regression method. The weighting factors of each parameter were calculated using occupants’ sensation and expectation to reflect the subjective preferences in model. Finally, an evaluation model expressing the indoor thermal, air quality and occupant preferences was developed to provide an HVAC intelligent control system that is more responsive to occupant needs.

Air damper with Controlling Capacity Unrelated to duct system resistance

The air volume control performance has an important impact on Heating, Ventilation and Air Conditioning (HVAC) system energy efficiency, indoor air quality and thermal comfort. However, one of the challenges in researching the air volume control of HVAC systems is that the controlling capacity of air dampers is related to the resistance of the entire duct system. The working flow characteristic (WFC) curve of the air damper varies across different duct systems, which strongly affects the damper control performance. Therefore, for this problem, this paper employed the combined CFD numerical simulation and experimental verification to obtain a novel air damper and its controlling formula. The novel air damper has good controlling capacity, and its flow characteristic curve does not change with the difference of the duct system. This paper offers a reference for the hydraulic balance of ventilation and air-conditioning systems and provides a new perspective for precise air volume control.

An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications

Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) price information, occupant thermal comfort, computational complexity, and nonlinear HVAC dynamics to design a demand response schema. In this paper, a novel two-stage integrated approach for such is proposed and evaluated. A model predictive control (MPC)-based optimiser for supervisory setpoint control is integrated with a digital parameter-adaptive controller for use in a demand response/demand management environment. The optimiser is designed to shift the heating load (and hence electrical load) to off-peak periods by minimising a trade-off between thermal comfort and electricity costs, generating a setpoint trajectory for the inner loop HVAC tracking controller. The tracking controller provides HVAC model information to the outer loop for calibration purposes. By way of calibrated simulations, it was found that significant energy saving and cost reduction could be achieved in comparison to a traditional on/off or variable HVAC control system with a fixed setpoint temperature.

Data mining approach for improving the optimal control of HVAC systems: An event-driven strategy

Heating, ventilation and air conditioning (HVAC) systems contribute to a major portion of energy consumption in buildings. Real-time optimal control is an efficient tool to improve the HVAC system efficiency. The formulation of the optimal control strategy is, however, challenging due to the lack of quantitative approaches and unified control format. Many previous studies use domain knowledge or experts’ interpretations to develop the optimal control strategy, which is qualitative, time-consuming and labor-intensive. This study proposes a data-mining-powered event-driven optimal control (EDOC) for improving HVAC operation efficiency. The main contributions are: (1) Provide a standard EDOC format to represent HVAC optimal control strategy; (2) Enable the automatic and quantitative formulation of optimal control strategies with minimal expert involvement. The random forest algorithm is adopted to discover event-driven relationships in the operation data. The effectiveness of the proposed approach is demonstrated through simulations. On average, the formulated EDOC strategy increases the energy saving by 0.9%–4.6% compared with a traditional time-driven optimal control. Meanwhile, events can be customized and can adapt to system renovations. The formulated EDOC is easy to use and can be easily understood by engineers and operators, which can be used to guide the optimal control of building HVAC systems.

A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

Influence of solar radiation on the energy consumption of large buildings on a university campus

This paper reports the numerical study on the influence of solar radiation on the energy consumption of large buildings on a university campus. The actual campus is located in the south of Portugal, in a Mediterranean type environment, and consists of 6 educational buildings. These six buildings have a total area of 27,599 m2 and 595 compartments, where 6,529 opaque surfaces (doors, walls, etc.) and 983 transparent ones (windows) were identified. This study aims to assess numerically how solar radiation transmitted on windows affects the energy consumption of the Heating, Ventilation, and Air-Conditioning (HVAC) systems, controlled by the PMV (Predicted Mean Vote) index, of each of these buildings, and the thermal comfort level of the occupants. Software developed by the authors is used to simulate the thermal behavior of buildings with complex topology. This software evaluates indoor air quality inside the spaces, thermal comfort of the occupants, thermal energy consumption of the HVAC system, and solar radiation distribution outside the buildings and inside the compartments, among others. The HVAC control system based on the PMV index applied in this work was designed to maintain the PMV comfort index within category C of ISO 7730, with a maximum of 15% of people dissatisfied. In order to evaluate the indoor comfort level of the occupants, the totals of cold and warm uncomfortable hours were calculated. Two different weather conditions, typical of the region, were set as inputs for the simulation performed in this study: a typical winter day, and a typical summer day. The outputs obtained were the daily evolution of total solar radiation transmitted on windows, total uncomfortable hours for the occupants, and total HVAC system energy consumption for each building. The results obtained show that, for typical winter conditions, an increase in the transmitted solar radiation on windows causes a decrease in HVAC system energy consumption, and also in the number of uncomfortable hours, which is a favorable situation. On the other hand, for typical summer conditions, it is observed that when transmitted solar radiation on windows increases, HVAC system energy consumption, and the total number of uncomfortable hours increase as well, configuring an unfavorable situation. It is also found that the values of solar radiation transmitted on windows are higher in winter than in summer conditions. In summer, the lowest values of solar radiation transmitted on windows occur at noon. The last two observations lead to the conclusion that, overall, these buildings have correctly positioned passive shading elements, a technique that contributes to an adequate solar passive architectural design.

Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system

Heating, ventilation and air conditioning (HVAC) systems are the most energy-consuming building implements for the improvement of indoor environmental quality (IEQ). We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation, through the fast prediction methods by using low-dimensional linear ventilation model (LLVM) based artificial neural network (ANN) and low-dimensional linear temperature model (LLTM) based contribution ratio of indoor climate (CRI(T)). To be continued for integrated control of multi-parameters, we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model (LLHM) and contribution ratio of indoor humidity (CRI(H)), and thermal sensation index (TS) for assessment. CFD was used to construct the prediction database for CO2, temperature and humidity. Low-dimensional linear models (LLM), including LLVM, LLTM and LLHM, were adopted to expand database for the sake of data storage reduction. Then, coupling with ANN, CRI(T) and CRI(H), the distributions of indoor CO2 concentration, temperature, and humidity were rapidly predicted on the basis of LLVM-based ANN, LLTM-based CRI(T) and LLHM-based CRI(H), respectively. Finally, according to the self-defined indices (i.e., EV, ET, EH), the optimal balancing between IEQ (indicated by CO2 concentration, PMV and TS) and energy consumption (indicated by ventilation rate, supplied air temperature and humidity) were synthetically evaluated. The total HVAC energy consumption could be reduced by 35% on the strength of current control strategies. This work can further contribute to development of the intelligent online control for HVAC systems.

Online fuzzy control of HVAC systems considering demand response and users’ comfort

ABSTRACT Heating, ventilation and air conditioning (HVAC) systems play an essential role in demand response (DR) programs. In this paper, a fuzzy controller is designed to adjust the HVAC set-points optimally. The aims of the designed controller are multifold: to save energy, to improve user’s comfort, and reduce HVAC electricity costs. In the current research, indices such as daily energy cost, minimum and maximum home temperature, energy usage, energy usage during peak hours, and user’s comfort are proposed and discussed for the evaluation of HVAC function. In addition, the effect of different pricing schemes such as fixed pricing (FP), time-of-use pricing (TOU), and real-time pricing (RTP), are analyzed. Further, the adaptability of the proposed model enabled us to investigate users with different attitudes toward welfare and cost. Finally, the effects of set-point and dead-band width are discussed. The results show that the proposed controller reaches the pre-determined aims successfully. Abbreviations: HVAC: Heating, ventilation, and air conditioning; FP: Fixed Pricing; TOU: Time Of Use; RTP: Real Time Pricing; PR: The HVAC system energy consumption cost in a day; UC: The total time that the user experiences an uncomfortable situation in a day; T_min: The minimum RealFeel Temperature experienced in a day by the user; T_max: The maximum RealFeel Temperature experienced in a day by the user; ECP: Energy consumed by the HVAC system during peak hours; EC: Energy consumed by the HVAC system during a day

Numerical simulation of the influence of external urban environmental conditions in the building windows performance

This paper presents a numerical study of the influence of external urban environmental conditions, namely, the solar radiation, in the building windows performance. A software that simulates the building thermal behaviour with complex topology, in transient conditions, is developed and used in the study of indoor air quality and indoor thermal comfort of the occupants of a building, under typical summer conditions. As management strategy was implemented a control system to the Heating, Ventilation and Air Conditioning (HVAC) using the PMV (Predicted Mean Vote) index as controllable variable. The studied university building is located in a Mediterranean-type climate in the south of Portugal. The indoor thermal comfort, evaluated by the PMV index, and the indoor air quality, evaluated by the carbon dioxide concentration, were obtained for all occupied spaces. In order to evaluate the implemented control strategy a set of results was obtained for the situations with and without HVAC system control. To exemplify the results obtained, two large compartments were chosen, one with windows facing South and the other without windows. As main conclusion, it can be stated that the use of the HVAC system controllable by the PMV index allows acceptable levels of thermal comfort within the category C of the ISO 7730 standard, and acceptable levels of indoor air quality within the limit proposed by the ASHRAE 62.1 standard.

Optimal price based control of HVAC systems in multizone office buildings for demand response

Optimizing the scheduling of heating, ventilation, and air-conditioning (HVAC) systems in multizone buildings is a challenging task, as occupants in various zones have different thermal preferences dependent on time-varying indoor and outdoor environmental conditions and price signals. Price-based demand response (PBDR) is a powerful technique that can be used to handle the aggregated peak demand, energy consumption, and cost by controlling HVAC thermostat settings based on time-varying price signals. This paper proposes an intelligent and new PBDR control strategy for multizone office buildings fed from renewable energy sources (RESs) and/or utility grid to optimize the HVAC operation considering the varying thermal preferences of occupants in various zones as a response of real-time pricing (RTP) signals. A detailed mathematical model of a commercial building is presented to evaluate the thermal response of a multizone office building to the operation of an HVAC system. The developed thermal model considers all architectural and geographical effects to provide an accurate calculation of the HVAC load demand for analyses. Further, Occupants’ varying thermal preferences represented as a coefficient of a bidding price (chosen by the occupants) in response to price signals are modeled using an artificial neural network (ANN) and integrated into the optimal HVAC scheduling. Furthermore, a control mechanism is developed to determine the varying HVAC thermostat settings in various zones based on the ANN prediction model results. The effect of the proposed strategy on aggregator utility with wider implementation of the developed mechanism is also considered. The optimization problem for the proposed PBDR control strategy is formulated using a building’s thermal model and an occupant’s thermal preferences model, and simulation results are obtained using MATLAB/Simulink tool. The results indicate that the proposed strategy with realistic parameter settings shows a reduction in peak demand varying from 7.19% to 26.8%, contingent on the occupant’s comfort preferences in the coefficient of the bidding price compared to conventional control. This shows that the proposed approach successfully optimizes the HVAC operation in a multizone office building while maintaining the preferred thermal conditions in various zones. Moreover, this technique can help in balancing the energy supply and demand due to the stochastic nature of RESs by cutting electricity consumption.

A Hierarchical HVAC Control Scheme for Energy-aware Smart Building Automation

Heating ventilation and air conditioning (HVAC) systems usually account for the highest percentage of overall energy usage in large-sized smart building infrastructures. The performance of HVAC control systems for large buildings strongly depend on the outside environment, building architecture, and (thermal) zone usage pattern of the building. In large buildings, HVAC system with multiple air handling units (AHUs) is required to fulfill the cooling/heating requirements. In the present work, we propose an energy-aware building resource allocation and economic model predictive control (eMPC) framework for multi-AHU-based HVAC system. The energy consumption of a multi-AHU-based HVAC system significantly depends on how long the AHUs are running, which again is governed by the zone usage demands. Our approach comprises a two-step hierarchical technique where we first minimize the running time of AHUs by suitably allocating building resources (thermal zones) to usage demands for zones. Next, we formulate a finite receding horizon control problem for trading off energy consumption against thermal comfort during HVAC operations. Given a high-level building specification and usage demand, our computer-aided design framework generates building thermal models, allocates usage demands, formulates the control scheme, and simulates it to generate power consumption statistics for the given building with usage demands. We believe that the proposed framework will help in early analysis during the design phase of energy-aware building architecture and HVAC control. The framework can also be useful from a building operator point of view for energy-aware HVAC control as well as for satisfying smart grid demand-response events by HVAC system peak power reduction through automated control actions.

Optimal Control Method for HVAC Systems in Offices with a Control Algorithm Based on Thermal Environment

This study examined a method to reduce energy consumption in office buildings. Correspondingly, an optimal control method was proposed for heating, ventilation, and air conditioning (HVAC) systems via two control algorithms that considered the indoor thermal environment. The control algorithms were developed by considering temperature and humidity as the factors of the indoor thermal environment that influence the control of HVAC systems and the predicted mean vote comfort ranges. Furthermore, an experiment was performed using office equipment that incorporated the two control algorithms for HVAC systems, and the correlation between changes in the thermal environment within the office and the occupant’s comfort levels was estimated via an actual survey. The results demonstrated that the proposed control method for HVAC systems, which considered the comfort ranges of temperature and humidity and the thermal adaptation capability, can efficiently maintain the occupant’s comfort with lower energy usage compared with conventional HVAC systems. Thus, the use of the control method contributes to the reduction of total energy consumption in buildings with HVAC systems.

HVAC Energy Cost Optimization for a Multizone Building via a Decentralized Approach

It has been well acknowledged that buildings account for a large proportion of the world's energy consumption. However, the energy use of buildings, especially the heating, ventilation and air-conditioning (HVAC), is far from being efficient. There still exists a dramatic potential to save energy through improving building energy efficiency. Therefore, this paper studies the control of HVAC system for multi-zone buildings with the objective to reduce energy consumption cost while satisfying thermal comfort. In particular, the thermal couplings due to the heat transfer between the adjacent zones are incorporated in the optimization. Considering that a centralized method is generally computationally prohibitive for large buildings, an efficient decentralized approach is developed, based on the Accelerated Distributed Augmented Lagrangian (ADAL) method [1]. To evaluate the performance of the proposed method, we first compare it with a centralized method, in which the optimal solution of a small-scale problem can be obtained. We find that this decentralized approach can almost approach the optimal solution of the problem. Further, this decentralized approach is compared with the Distributed Token-Based Scheduling Strategy (DTBSS) [2]. The numeric results reveal that when the number of zones is relatively small (less than 20), the two decentralized methods can achieve a comparable performance regarding the cost of the HVAC system. However, with an increase of the number of zones in the building, the proposed decentralized approach demonstrates better performance with a considerable reduction of the total cost. Moreover, the decentralized approach proposed in this paper demonstrate better scalability with less average computation required.

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings.

The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach.

Building temperature regulation in a multi-zone HVAC system using distributed adaptive control

During recent years there have been considerable research efforts on improving energy efficiency of buildings. Since Heating, Ventilation and Air-Conditioning (HVAC) systems are responsible for a big part of energy consumption, developing efficient HVAC control systems is crucial. In most of the developed approaches, precise knowledge of system parameters and/or adequate historical data is required. However, these approaches may not perform as well in the presence of dynamic parameter changes due to human activity, material degradation, and wear and tear, or disturbances and other operational uncertainties due to occupancy, solar gains, electrical equipment, and weather conditions. In this paper, we consider buildings with several climate zones and propose a distributed adaptive control scheme for a multi-zone HVAC system which can effectively regulate zone temperature by applying on-line learning and assuming exchange of information between neighboring zones. The controller of each zone achieves the local objective of controlling zone temperature by compensating for the effects of neighboring zones as well as for possible changes in the parameters of the system. Despite the exchange of information, each local controller does not know how the control actions and temperature of a neighboring zone affect the temperature of its own zone. For this reason, each local controller is estimating the parameters of the interconnections in real time and uses them together with the exchanged information to provide a more accurate local zone temperature control. The proposed method is illustrated using an example of temperature control in a six-zone building as well as a large school building, which are implemented in a Building Controls Virtual Test Bed (BCVTB) environment using EnergyPlus and MATLAB/Simulink.

Energy saving impact of occupancy-driven thermostat for residential buildings

Heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Since most people do not have the expertise to control the HVAC system efficiently, unnecessary energy consumption is often caused by wasteful behaviors (overheating/overcooling and operation without occupancy). New Internet of Things (IoT) products for a smart home have the potential for energy saving by reducing the unnecessary operation of the energy system in a residential house. However, many people still hesitate to adopt this product with uncertain economic benefit. This work explores a co-simulation platform to assess energy saving impact and economic benefits of occupancy driven thermostat in a residential house. An occupancy simulator was devised and utilized to consider the random nature of the occupancy in a typical single family residential house. Six HVAC system control strategies were explored based on three types of thermostats (always on; schedule based; and occupancy driven) as well as two setpoint control algorithms (fixed setpoint; and adaptive control). Energy-plus was integrated into the co-simulation platform, which evaluated energy consumption and indoor temperature of a residential house in five climate conditions. User's comfort level was evaluated using the adaptive model for the six different control strategies in each location. The result showed that the occupancy information-based control algorithm can save about between 11% and 34% of energy without significantly risking the occupant's comfort level. The work also suggests that the adaptive control model has a more tangible saving impact as IoT products can easily integrate outside temperature. Depending on the locations, adaptive control can save up to 54% of energy consumption, and the occupancy information can add additional energy saving impact by 20%. Payback period varies for different control strategies and depends on the rate of utility as well as the amount of energy saving. Compared with the most wasteful control strategy (fixed setpoint - always on), adding a thermostat with adaptive – occupancy driven control strategy can lead to less than one year of payback period regardless of location.

IoT Based Indoor Air Quality and Smart Energy Management for HVAC System

Abstract Heating ventilation and Air Conditioning (HVAC) systems consume a substantial volume of energy within corporate buildings, mainly due to lack of severe monitoring which results in compromising either energy efficiency or user comfort. We propose a simple HVAC control system that automates the HVAC operation in real time by considering energy management policies and user preferences. Our system is built on top of IoT (Internet of Things) framework, where appliances in a laboratory are automated with suitable sensors also thermal parameters are obtained from sensors and user feedback information are collected for real-time processing in our distributed cloud environment. We utilized Blynk Application Programming Interface (API) to monitor the real time data from sensors, to obtain user feedback periodically and to dynamically adjust the temperature settings based on energy management policies, user feedback and sensor values. The effectiveness of HVAC is evaluated mathematically. Our experiments indicate that we achieve total saving of 0.9 kWhr and also maintain user thermal comfort consistently.

Application of Artificial Neural Network for the Optimum Control of HVAC Systems in Double-Skinned Office Buildings

Double Skin Façade (DSF) systems have become an alternative to the environmental and energy savings issues. DSF offers thermal buffer areas that can provide benefits to the conditioned spaces in the form of improved comforts and energy savings. There are many studies conducted to resolve issues about the heat captured inside DSF. Various window control strategies and algorithms were introduced to minimize the heat gain of DSF in summer. However, the thermal condition of the DSF causes a time lag between the response time of the Heating, Ventilation, and Air-Conditioning (HVAC) system and cooling loads of zones. This results in more cooling energy supply or sometimes less than required, making the conditioned zones either too cold or warm. It is necessary to operate the HVAC system in consideration of all conditions, i.e., DSF internal conditions and indoor environment, as well as proper DSF window controls. This paper proposes an optimal air supply control for a DSF office building located in a hot and humid climate. An Artificial Neural Network (ANN)-based control was developed and tested for its effectiveness. Results show a 10.5% cooling energy reduction from the DSF building compared to the non-DSF building with the same HVAC control. Additionally, 4.5% more savings were observed when using the ANN-based control.

Optimal chiller loading in HVAC System Using a Novel Algorithm Based on the distributed framework

Aimed at the optimal chiller loading (OCL) problem in heating, ventilation and air conditioning (HVAC) system, a distributed framework for HVAC control is introduced and discussed. And furthermore, the distributed chaotic estimation of distribution algorithm (DCEDA) based on this framework is proposed. Firstly, compared with the centralized framework, the distributed framework has the features of flexibility and expansibility. Therefore, the distributed framework can fit the development of HVAC control system better. Secondly, in the proposed algorithm, an initialization methodology based on logistic map is developed and the chaotic mutation mechanism is applied to increase the search capability of the algorithm. To testify the performance of DCEDA, two well-known cases based on the OCL problem are tested and the results are compared with other algorithms. The results show that DCEDA is an efficient distributed optimization algorithm with good robustness, stability and convergence, and it can achieve significant energy saving effect.

Closed-Loop Identification for Model Predictive Control of HVAC Systems: From Input Design to Controller Synthesis

Heating, ventilation, and air conditioning (HVAC) systems are responsible for maintaining occupants’ thermal comfort and share a large portion of the overall building energy use. Hence, it is of great interest to improve the performance of HVAC control systems and thus the building energy efficiency. Model predictive control (MPC) has been proved to be a promising control strategy to be employed in this field. However, MPC implementation relies on the model of the system, and inaccurate models can deteriorate the control performance, while overly complicated models can lead to the prohibitive computational burden. Because of this, existing models do not usually allow the MPC controller to adjust multiple set points (e.g., both temperature and flow rates) and do not include the dynamics of the heating and ventilation subsystems with their local controllers. In this paper, we address the challenge of developing more reliable HVAC models for MPC controllers based on the experimental data. Data are obtained from an experiment designed using a graph theoretical technique, which guarantees maximum information content in the data. The resulting models are employed to design local controllers of the heating and ventilation subsystems, which are experimentally tested in a real HVAC test bed. A supervisory MPC controller that incorporates the closed-loop models of the heating and ventilation subsystems is then developed. This can lead to a control strategy able to more effectively adapt key HVAC set points based on weather conditions, occupancy, and actual thermal comfort, as shown by a numerical study based on the data from the HVAC test bed.