Multi-zone Variable Air Volume Research Articles

Model-based optimal control strategy for multizone VAV air-conditioning systems for neutralizing room pressure and minimizing fan energy consumption

Control strategies for variable air volume (VAV) air-conditioning systems play a pivotal role in ensuring indoor environmental quality and energy efficiency. However, conventional approaches, such as static pressure reset (SPR) control, focus on managing indoor air temperature without considering the room pressure, which can lead to unbalanced room pressure and undesirable air leakage. Moreover, with the application of prevalent building pressure control strategies, such as airflow tracking control, to multizone VAV systems, neutralization of the room pressure is difficult across multiple zones in VAV systems. Therefore, this study introduces a model-based optimal control strategy for multizone VAV air-conditioning systems. The proposed strategy uses a multiobjective optimization framework to regulate fan frequencies and damper openings on both the supply and return sides. This holistic approach facilitates the simultaneous control of the indoor air temperature and room pressure while minimizing fan energy consumption. To assess the effectiveness of the proposed strategy, four control strategies were tested using a Python-based simulation testbed. The results demonstrate that the proposed strategy effectively maintains the indoor air temperature, neutralizes room pressure, and reduces fan energy consumption, thereby contributing to the overall efficiency of the VAV system. Moreover, the results highlight the limitations associated with combining airflow tracking control with SPR control for room pressure regulation in multizone VAV systems. This highlights the importance of adopting a model-based approach to address the complexities of concurrent room pressure and indoor air temperature control.

Global hydraulic stability analysis for dynamic regulation in multi-zone variable air volume air-conditioning system

Variable air volume air-conditioning system (VAV system) has multiple control loops that interfered with each other, which has serious impacts on its actual operation effect. To solve this problem, the index of global hydraulic stability, which takes the physical quantity of the pipe network pressure and the air flow rate as reference, is proposed based on Graph theory, and a pipe network model for the hydraulic calculation is built. Then, the simulation study based on the actual operation data is carried out in MATLAB. The distribution of the pressure and the air flow in the pipe network is obtained, and the action interferences among adjacent terminal boxes are analyzed based on global hydraulic stability under damper position regulation strategy (DP strategy) and fan frequency regulation strategy (FF strategy). Results indicate that the FF strategy can decrease the total change distance of the damper position compared with the DP strategy, and then the fluctuation of the pressure and the air flow can be reduced. The contribution of this study is to provide an evaluation index of global hydraulic stability, which can also be used in other air-conditioning systems with multiple terminals and multiple control loops.

Benchmarking high performance HVAC Rule-Based controls with advanced intelligent Controllers: A case study in a Multi-Zone system in Modelica

The design, commissioning, and retrofit of heating, ventilation, and air-conditioning (HVAC) control systems are crucially important for energy efficiency. However, designers and control contractors adopt ad-hoc control sequences based on diffused and fragmented information; therefore, most of the existing control sequences are diverse and sub-optimal. ASHRAE Guideline 36 (GDL36), High-performance Sequences of Operation (SOO) for HVAC Systems, was thus developed to provide standardized and high-performance rule-based HVAC control sequences with the main focus on maximizing energy efficiency. However, only limited studies verify the performance of these high-performance rules-based control and most of these studies focus on the single-zone systems. In this Modelica-based simulation study, the high-performance rule-based control sequences from GDL36 were compared to the state-of-the-art intelligent controllers (i.e., optimization-based controller (OBC) and deep reinforcement-learning-based controller (DRLC)) in terms of the energy efficiency and thermal comfort. The performance evaluation was conducted in a medium-sized office building with a multi-zone Variable Air Volume (VAV) cooling system. The OBC and DRLC replaced the GDL36 airside supervisory level control loops. In other words, the optimal supply air temperature (SAT) and static differential pressure (DP) setpoints were determined by the optimization problems in OBC and trained control policy by DRLC. The OBC and DRLC were formulated to minimize the HVAC energy consumption and zone air temperature violation. The OBCs with different control intervals and DRLCs with different hyperparameters in the Proximal Policy Optimization (PPO) algorithm were exploited and fine-tuned. The simulation results show that the GDL36 has a comparable energy performance (within a 3% deviation) with DRLC in the cases with high or mild cooling loads. It also has a comparable energy performance (within a 3% deviation) with OBC in the case of a high cooling load. However, the case with GDL36 consumed 7% more energy in the testing shoulder period. For the thermal comfort metric, the GDL36 has slightly more zone air temperature violations in all testing scenarios compared to the two intelligent controllers. From this case study, we can conclude that the GDL36 has demonstrated its comparable performance in terms of energy efficiency and thermal comfort with the two intelligent controllers. The GDL36 airside SOO is good enough considering the complexity and tuning efforts of the intelligent controllers.

Thermal sensation and occupancy-based cooperative control method for multi-zone VAV air-conditioning systems

Most existing multi-zone variable air volume (VAV) air-conditioning systems rely on manual adjustment or a constant temperature set point, with no consideration for users' thermal sensation. VAV terminals operate regardless of whether there is a demand for cooling in the zone, rather than according to the actual occupancy situation between multiple zones, resulting in poor indoor thermal comfort and the high energy consumption of air-conditioning systems. This paper proposes a thermal sensation and occupancy-based cooperative control (TSOCC) method for multi-zone VAV air-conditioning systems in order to provide an indoor thermal environment more in line with the real-time needs of personnel, while also increasing cooperative control between multiple zones to ensure that VAV terminals do not operate unnecessarily when zones are unoccupied. To evaluate the control performance of this method, a simulation platform is built, case simulations with different zone occupancy rates are carried out and various comparisons and analyses are made with temperature set point-based control. The results show that the TSOCC method for multi-zone VAV air-conditioning systems can provide an indoor thermal environment more in line with the thermal comfort needs of the personnel and save 6–14% of daily system energy consumption compared with the temperature set point-based control strategy, while the system's energy-saving rate increases as the zone occupancy rate decreases, marking a change from whole zone-oriented cooling to zone-oriented cooling and providing more accurate air-conditioning services for indoor personnel.

Model predictive control for multi-zone Variable Air Volume systems based on artificial neural networks

In this paper, an artificial neural network (ANN) based system-level model predictive control framework is established for a variable air volume (VAV) system to improve its robustness and energy efficiency. The VAV system consists of three processes: the zone temperature process, the damper process and the supply air volume process of the air handling unit connecting the ductwork for all of the boxes. An ANN-based predictive controller is designed for the zone temperature process, and a PI controller is used for the damper process; they operate in a cascaded manner. Another ANN predictive controller tracks the total supply air volume subject to the cooling load constraints from the lower level VAV boxes and minimizes the fan energy consumption. The ANN controllers are optimized online by the Lagrange variational approach (LVA) to minimize the system cost function and implement a feedback receding horizon optimal control. With the LVA based gradient descent training algorithm, the weight convergence of the ANN predictive controllers and stability of the control system is obtained with small computational and storage needs. The modeling methodology, based on ANNs for the above processes, is developed and calibrated with actual data measured from a site. All of the data processing and control strategy implementations are based on extensive measurements collected from a laboratory building located in the campus of Beijing University of Civil Engineering and Architecture, Beijing, China. The experimental results show that an additional 6.12% energy savings is achieved. This is accomplished by introducing the fan control signal into the system cost function as the performance index of energy consumption with a 0.2 penalty coefficient. In the presence of un-modeled disturbances and modeling errors, the framework also reduces the temperature oscillation at least by approximately 60% compared with PI control.

Predictive control of multi-zone variable air volume air-conditioning system based on radial basis function neural network

The multi-zone variable air volume (VAV) air-conditioning system is a complex thermal system with large delay and nonlinearity. Due to the complex environment of multi-zone buildings and the complicated operation process of the VAV air-conditioning system, there are many difficulties in the room temperature control. This paper firstly establishes a multi-zone building model for room temperature using resistance–capacitance method. This investigation simulates and measures the dynamic response of room temperature in a three-floor building without/with air-conditioning for validation. Then a multi-zone VAV air-conditioning system room temperature predictive control model based on radial basis function (RBF) neural network (NN) is proposed. This study sets up a multi-zone VAV air-conditioning system experimental platform in the three rooms on the first floor of the building and implements the predictive control model based on the RBF neural network. The experimental results show that the predictive control model based on RBF NN is able to meet room temperature requirements. It also has strong anti-interference performance and ensures stable static pressure of the main air supply duct. The multi-zone building model can accurately simulate the temperature changes of each room when the air supply volume varies.

Estimating ASHRAE Guideline 36 energy savings for multi-zone variable air volume systems using Spawn of EnergyPlus

ASHRAE Guideline 36 (G36) publishes high-performance control sequences for Variable Air Volume (VAV) system operation. Retrofitting existing VAV control sequences to G36 promises to have a large potential for energy savings. However, it is difficult to estimate the savings accurately and the process of doing so can be costly and time-consuming. This paper evaluates the energy use of a multi-zone VAV system with terminal reheat using the G36 sequences and compares it to a group of baseline control sequences that represent existing practices. Spawn of EnergyPlus is used for the whole building simulation, where the envelope is modelled in EnergyPlus and the HVAC equipment and its pressure-flow network and the control sequences are modelled in Modelica. The comparison of the control sequences performance is further conducted in parametric studies. For a medium-sized commercial building, the G36 sequences provide a wide range of HVAC energy savings with an average of 31%.

Optimal Dynamic Duct Static Pressure Method in a Multi-Zone Variable Air Volume System

Reducing the energy consumption of a variable air volume (VAV) system in heating, ventilation, and air-conditioning (HVAC) systems attracts many attentions. In this letter, a novel method, namely, optimal dynamic duct static pressure (ODSP) method is proposed to find the globally optimal solutions of the original non-convex multi-zone VAV system energy optimization problem. Our method is based on the primal dual interior point method to solve an equivalent convex optimization problem of the multi-zone VAV system energy optimization problem. Different from existing methods, our method is a real-time optimization method and explicitly considers the duct network model to find the energy saving strategy. Numerical simulations illustrate the effectiveness of our method compared with current methods, which show that our method provides better energy efficiency and nearly 20% energy is saved. Our method also has the guarantee to find the optimal solutions.

What are the impacts on the HVAC system when it provides frequency regulation? – A comprehensive case study with a Multi-Zone variable air volume (VAV) system

Heating, ventilation, and air-conditioning (HVAC) systems in commercial buildings have proven to be a great asset for providing secondary frequency regulation (FR) service to the power grid. However, how the provision of FR service affects the HVAC system performance is still largely unknown. This paper investigates how a HVAC system performance and its associated controls are impacted during and after a FR service. We exploit a dynamic Modelica-based medium office building model as a virtual testbed with the Air Handling Unit (AHU) fans providing the FR service for the assessment. The virtual testbed adopts state-of-the-art HVAC control sequences and an experimentally validated frequency regulation control scheme. The comprehensive study of 832 case studies considers several scenarios, consisting of different regulation capacities (demand levels), standardized FR test signals, and building load profiles in 16 U.S, climate zones. The holistic assessment includes the interaction between the FR and the HVAC baseline controls, the impact of FR on the occupant’s thermal comfort, and, more importantly, the energy efficiency.The results show that FR has little or no impact on the HVAC operation and occupant thermal comfort while the building is providing the service if an appropriate regulation capacity is pre-determined. However, if the regulation capacity is overestimated, the quality of the FR service provided by the HVAC system and the HVAC energy efficiency will be affected due to the equipment operating constraints. In addition, the AHU supply air temperature fluctuates, and the controller cannot maintain the desired setpoint as the FR magnitude is increased. After the FR service time, the system takes a certain period to return to normal operation. The rebound period extends with the increase in the FR magnitude. For this case study, there exists a control conflict between the FR control and the existing HVAC control (from ASHRAE Guideline 36-2018) during high load conditions, causing the fan speed to saturate. The increased fan electricity consumption caused by this control conflict will offset the potential revenue a building can earn from offering the FR service. In addition, the rebound power usage could have a negative effect on the grid.

A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method

Determining the proper trade-off among thermal comfort, Indoor Air Quality (IAQ) and energy use is important for optimal control of air-conditioning systems. The number of optimization variables increases as systems become increasingly complex, as with multi-zone VAV (Variable Air Volume) air-conditioning systems, leading to large-scale mathematics programming challenges and inconveniences in the implementation of conventional centralized optimization strategies. This paper therefore proposes a real-time optimal control strategy adopting a multi-agent based distributed optimization method for multi-zone VAV air-conditioning systems. The proposed strategy consists of three novel schemes. First, a temperature set-point reset scheme adopts a linear rule to correlate the resetting of the temperature set-points in individual zones to simplify the optimization problem while applying proper optimization in individual zones. Second, a multi-objective optimization scheme optimizes the fresh air ratio of the supply air and the temperature set-point in the critical zone by formulating the multi-objective optimization problem. Third, a multi-agent distributed optimization scheme is developed to solve the optimization problem in a distributed manner, facilitating the deployment of local control devices of limited capacity. A TRNSYS-MATLAB co-simulation testbed is constructed to test and validate the proposed strategy. Test results show that the strategy is effective in properly balancing thermal comfort, IAQ and energy use while largely reducing programming challenges. The distributed optimization method can provide almost the same optimal outputs as conventional centralized optimization methods.

Comparative study of outdoor airflow requirement and distribution in multi-zone VAV system with different control strategies

Owing to design and operation limitations, most multi-zone variable air volume (VAV) systems in existing buildings still follow traditional techniques for ventilation. In recent ventilation research, most studies focused on comparing a traditional strategy with a superior one, and the comparison studies among traditional strategies are relatively rare. In this paper, four traditional outdoor airflow control strategies that are commonly used in existing VAV systems are introduced and discussed. The results illustrate that the phenomenon of uneven distribution for outdoor airflow among conditioned spaces often occurs, irrespective of the strategies with which the system operates. Compared to constant outdoor airflow fraction strategies, variable outdoor airflow fraction strategies can meet system outdoor airflow requirements while the occupancy number varies. On this basis, performance with respect to IAQ and energy-savings of variable outdoor airflow fraction strategies is compared. The results indicate that the two strategies have the same control effect on the IAQ. Furthermore, the multiple spaces equation-based strategy (MSE-V) shows energy reductions of 6.76% and 9.88% in the heating and cooling seasons, respectively, compared to the maximum outdoor airflow fraction-based strategy (MaxY-V) when the system operates under the same operation conditions.

Energy savings of retrofitting residential buildings with variable air volume systems across different climates

Residential buildings account for 20% of total energy consumption in the United States, with single family detached houses accounting for 77% of residential site energy consumption. A multizone variable air volume (VAV) system can save energy by directing conditioned air to different occupied zones in the home as needed. While multizone VAV systems in single family houses have been economically inaccessible in the past, recent technological developments in building automation controls and the Internet of Things (IoT) can enable homeowners to retrofit their existing HVAC systems into VAV systems, warranting further investigation into the energy savings potential of such systems. Past residential VAV system research mostly involved simulations and measurements of singular case study houses over short periods of time. In this study, representative energy models of two different house sizes, with estimates of individual room sizes, were developed to expand on those case studies and examine energy savings for single family homes across seven US climate zones over an entire year. Average size house models report 24%–42% source energy savings while large house size models report 18%–35% source energy savings, with houses in cooling dominant climate saving relatively more. Annual energy cost savings range from 24% to 42% for average size house models and 18%–35% for large size house models. These results indicate greater energy savings potential for single family homeowners considering VAV retrofits in cooling climates and areas with higher cost of electricity. Projected present value of energy cost savings over a typical home ownership period similarly yielded favorability for houses in cooling dominant climates, but were impacted by energy price changes over time. Future research should focus on the payback analyses of single family VAV system implementations and its sensitivity to occupant behavior. The details of the representative energy models, with estimates of room sizes and occupancy schedules, are provided for future studies that focus on multizone energy saving strategies.

Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems

Variable air volume (VAV) is the most common installation among heating ventilating and air conditioning (HVAC) systems. To maintain the comfort level and lessen energy utilization, there is a pressing need for its effective control. In this study, a lumped parameter model composed of multizone VAV is considered, and sliding mode control (SMC) is designed to guarantee robust operation in the presence of uncertainties. For comparison of the proposed controller performance, a proportional integral derivative (PID) controller is additionally designed. The indoor temperature of zones is controlled by positioning the supply air dampers. Tracking objectives of controllers are inspected via two practical cases of desired temperature setpoints including (a) sinusoidal waveform and (b) the combination of steps. Results obtained using SMC ensure the robust operation of the VAV system against parametric uncertainties. In addition, SMC is more energy efficient than PID in terms of overshoot and settling time.

Evaluation of a cost-responsive supply air temperature reset strategy in an office building

This paper describes a new supply air temperature control strategy for multi-zone variable air volume systems. We developed the strategy with the intent that it is simple enough to implement within existing building management systems. At 5-min intervals, the strategy estimates the cost of fan, heating and cooling energy at three different supply air temperatures (current, higher, lower), and chooses the one with the lowest cost as the setpoint. We then implemented this strategy in a seven floor, 13,000m2 office building and compared the energy costs to the industry best practice control strategy in a randomized (daily) controlled trial over a 6-month period. We showed that the new control strategy reduced total HVAC energy costs by approximately 29%, when normalized to the typical annual climate data for this location and operating only during typical office hours. These findings indicate that the current industry best practice control strategy does not find the optimal energy cost point under most conditions. This new control strategy is a valuable opportunity to reduce energy costs, at little initial expense, while avoiding more complex approaches, such as model predictive control, that the industry has been hesitant to adopt. We describe the new control strategy in language common to the industry (see sequence of operations included as supplemental material) so that readers may easily specify and implement this immediately, in new construction or controls retrofit projects.

Global optimal control of variable air volume air-conditioning system with iterative learning: an experimental case study

The air-conditioning system in a large commercial or high-rise building is a complex multi-variable system influenced by many factors. The energy saving potential from the optimal operation and control of heating, ventilating, and air-conditioning (HVAC) systems can be large, even when they are properly designed. The ultimate goal of optimization is to use the minimum amount of energy needed to improve system efficiency while meeting comfort requirements. In this study, a multi-zone variable air volume (VAV) and variable water volume (VWV) air-conditioning system is developed. The steady state modes and dynamic models of the HVAC subsystems are constructed. Optimal control based on large scale system theory for system-level energy-saving of HVAC is introduced. Control strategies such as proportional-integral-derivative (PID) controller (gearshift integral PID and self-tuning PID) and iterative learning control (ILC) are studied in the platform to improve the dynamic characteristics. The system performance is improved. An 18.2% energy saving is achieved with the integration of ILC and sequential quadratic programming based on a steady-state hierarchical optimization control scheme.

Variable air volume air-conditioning experiment system with advanced controls

A heating, ventilation, and air-conditioning (HVAC) system is a multi-variable strongly coupled large-scale system that is composed of several sub-systems. Considerable research, simulations, and experiments have been conducted on HVAC control. The optimization control of an HVAC system is now the popular issue. The ultimate goal of this paper was to achieve minimum energy consumption and improve system efficiency. Multi-zone variable air volume and variable water volume air-conditioning systems were developed. The dynamic models of HVAC sub-systems were built by the adaptive directional forgetting method. Control strategies such as the gearshift integral proportional-integral-derivative (PID) controller and self-tuning PID controller were studied in the platform to improve the dynamic characteristics of the HVAC system. System performance was improved. The system saved 18.2% of energy with the integration of iterative learning control and sequential quadratic programming based on the steady-state hierarchical optimization control scheme.

Model-based Optimal Control of Outdoor Air Flow Rate of an Air-Conditioning System with Primary Air-Handling Unit

This paper presents a model-based outdoor air flow rate optimal control strategy for multi-zone variable air volume air-conditioning systems with the primary air-handling units. An adaptive optimisation algorithm is adopted for optimising the set point of the outdoor air flow rate to minimise the energy cost, which could compromise the energy consumption of the primary fan and the cooling energy saving by the cold outdoor air. The primary fan energy consumption can be predicted using a simplified incremental fan model and the main parameters of this model are identified online. The cooling energy saving by the outdoor air is estimated online using the enthalpies of the air streams. The lower limit of the outdoor air flow rate is determined by a CO2-based adaptive demand-controlled ventilation strategy using the dynamic multi-space equation to maintain the satisfied indoor air quality (IAQ). Tests were conducted to evaluate the performance of the control strategy applied to a practical building system in simulation environment. The results show that the proposed optimal control strategy can reduce energy consumption significantly, while maintaining a satisfactory IAQ.

Online Optimal Ventilation Control of Building Air-conditioning Systems

This paper presents a ventilation control strategy for multi-zone variable air volume (VAV) air-conditioning systems; integrating the sequential split-range control strategy for air-handing units with an aim to optimise the fresh air flow rate by compromising the indoor air quality and energy consumption. In this strategy, a CO2 -based adaptive demand-controlled ventilation scheme would employ a dynamic multi-zone ventilation equation for multi-zone air-conditioning systems, in which a CO2-based dynamic occupancy detection scheme would be used for online occupancy detection. The strategy would identify the critical zones online, and fully consider the outdoor air demand of critical zones, while, a model-based fresh air flow rate optimal control scheme is employed for VAV air-conditioning systems with the primary air handling units. An adaptive optimisation algorithm would be used for optimising the fresh air flow rate to minimise the energy consumption. The energy saving potentials in the Hong Kong climate condition by optimising fresh air ventilation and the practical implementation of the control strategy are also discussed in this paper.

Experimental Analysis of a Genetic-Fuzzy Inverter DX VAV A/C System for Automatically Ventilated Buildings

In recent years, the quest has been focused on energy efficient building design. To achieve this in terms of high efficiency air conditioning schemes for hot climate cooling, the combination of variable refrigerant volume (VRV) with variable air volume (VAV) systems have become popular. In this paper, attention is focused on achieving good thermal comfort and indoor air quality (IAQ) combined with energy savings by using multi-zone VAV air conditioning (A/C) that incorporates a genetic based fuzzy logic controller (FLC). Experimental analysis based on a combined demand controlled ventilation (DCV) with economizer cycle (EC) was performed on an inverter driven multi-zone direct expansion (DX) VAV A/C system integrated with a fuzzy logic controller and optimized by a genetic algorithm (GA). The opening of the VAV box damper was controlled using the fuzzy logic controller. Based on the test results, the proposed fuzzy logic operated system maintained supply air temperature close to 13°C and an occupant zone at a consistent temperature of around 24 °C. In DCV mode, the concentration of CO2 was maintained between 950 ppm and 1040 ppm while, when the system was operated under a combined DCV-EC ventilation scheme, the CO2 concentration was maintained at between 350 ppm and 970 ppm. The experimental results suggested that CO2 concentration obtained experimentally was well within the permissible limit for the varying load conditions. Under the combined DCV-EC mode of ventilation, using a fuzzy-genetic algorithm, the maximum power obtained for the supply air fan and variable speed compressor was 415 W and 3.4 kW respectively. The compressor was totally turned OFF during the economizer cycle thus contributing to total power savings. The energy savings potential of the proposed fuzzy controlled multi-zone DX VAV A/C system yielded 70% and 89% under DCV and combined DCV economizer cycle ventilation modes respectively when compared with a constant air volume (CAV) A/C system operated under the DCV technique. The test results suggested that it was feasible for this fuzzy control methodology, integrated with the developed genetic algorithm, to provide a proper control of IAQ, thermal comfort and energy conservation.

Qualitative model-based fault detection in air-handling units

The feasibility of a qualitative approach for detecting faults in an air-conditioning system is considered. The system considered is a multi-zone variable air volume air-handling unit, and the faults investigated include types which result in deterioration of operation, as distinct from actual failure. The operating modes of the sequential controller for the central air-handling plant can be matched to a corresponding qualitative classification of steady-state temperatures. Observed mismatches indicate the presence of faults. Trials of the method in an air-conditioning test laboratory are reported. >