Optimal Supply Air Temperature Research Articles

Optimal supply air temperature with respect to data center operational stability and energy efficiency in a row-based cooling system under fault conditions

This is the first study of data center row-based cooling strategies under fault conditions. An optimal supply air temperature (SAT) for row-based cooling is proposed to ensure energy efficiency and operational reliability. The appropriate SAT for emergency operation is determined by evaluating how long the allowable IT equipment's inlet air temperature (IAT) can be maintained by only running the fan of the computer room air handling (CRAH) unit when the chilled water supply is interrupted. Three cases with SAT values of 20 °C, 22 °C, and 24 °C are evaluated in a numerical analysis, and the maintenance potential of the IAT is assessed by analyzing 600 s of transients from the time of cooling failure. In all cases, the IAT is almost the same as the SAT under normal cooling, but in cooling failure, the time to reach downtime beyond the allowable IAT is 246s in Case 1, 133s in Case 2, and 85s in Case 3. Response strategies to cooling failure include increasing uninterruptible power supply (UPS) capacity and applying a thermal buffer tank. The study concludes that it is reasonable to set the SAT of row-based cooling to 22 °C or lower and to install a thermal buffer tank in the chilled water system for effective downtime delay. These results are based on a rack power density of 7 kW/rack.

Optimization-informed Rule Extraction for HVAC system: A Case Study of Dedicated Outdoor Air System Control in a Mixed-Humid Climate Zone.

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.

Solutions to Achieve High-Efficient and Clean Building HVAC Systems

The building sector accounts for a substantial amount of energy consumption, resulting in higher carbon emissions and environmental impact worldwide. Electrification and energy efficiency in building systems can be the key to decarbonization in buildings. This research proposes new heating and cooling loops consisting of heat pumps to lower natural gas usage in building systems. Typical chillers and boilers in the cooling and heating loops are replaced with heat pumps to serve the loads and maintain thermal comfort in the building. In addition, a new optimal supply air temperature (SAT) reset strategy is also implemented with the proposed configuration for better system performance. A large multi-zone office building is simulated as a case study to measure the conventional system’s electricity and natural gas consumption and the proposed design. Even with heat pumps that use electricity as the energy source, electricity consumption is reduced by 3.3% to 11.8% in different climate zones for the proposed system. On the other hand, 10.2% to 67% lower natural gas is consumed when the proposed system and the optimal SAT reset are utilized. The carbon emission is also reduced by 10.8% to 38% compared to the conventional system. The results show that the proposed design and optimization strategy can lead to significant energy and cost savings in conjunction with lower carbon emissions.

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.

Cooling effect of air movement on heating performances of advanced air distribution

The cooling effect of air movement is widely recognized as an energy-efficient measure for thermal environment management under the cooling mode, but it is generally neglected under the heating mode. This study proposes that the cooling effect of air movement negatively impacts the energy efficiency of advanced air distribution and should be considered when modulating advanced air distribution under the heating mode. Advanced air distribution like stratum ventilation and wall attachment ventilation supplies warm and clean air into the occupied zone by employing supply air momentum to suppress thermal buoyancy. However, the supply air momentum elevates the air movement in the occupied zone and the cooling effect of air movement negatively impacts the energy efficiency of advanced air distribution under the heating mode. Based on numerical simulations validated by the experiments, the results show the cooling effect of air movement significantly impacts the optimal operation, particularly the optimal supply airflow rate of stratum ventilation and the optimal supply air temperature of wall attachment ventilation. Accounting for the cooling effect of air movement can improve the optimal overall performance of advanced air distribution, particularly when the preference for energy efficiency increases. Compared with the optimization ignoring the cooling effect of air movement, the optimization considering the cooling effect of air movement improves the overall performance by up to 38.7% for stratum ventilation and up to 67.6% for wall attachment ventilation. Therefore, the cooling effect of air movement should be considered for modulating advanced air distribution under the heating mode.

Climate Compensation and Indoor Temperature Optimal Measuring Point Energy Saving Control in VAV Air-Conditioning System

Temperature measuring point is the key to room environment control. Temperature measuring points and climate changes are directly related to the room control effect. It is of great theoretical and practical significance to study the temperature measuring points and control strategy based on climate compensation. In this study, first, the climate compensation concept in a heating system was introduced into a variable air volume (VAV) air-conditioning system. The heating load was modeled as a function of supply air temperature by analyzing the heat exchange. Based on each control link of subsystems, a climate compensation scheme is proposed to determine the optimal set-point of the supply air temperature. At the same time, a layout of multiple temperature measuring points of an air-conditioned room was studied. Furthermore, the optimal indoor temperature measuring point was determined using an adaptive weighted fusion method. Finally, simulation results show that the proposed method has better control effects on indoor temperature adjustment compared with the traditional method. The optimal supply air temperature in summer and winter was determined according to the proposed climate compensation scheme, and the supply air temperature was controlled using an improved single-neuron adaptive control strategy. Experimental results show that the maximum energy saving can reach up to 35.5% in winter and 6.1% in summer.

Multi-criteria performance optimization for operation of stratum ventilation under heating mode

Stratum ventilation with proper operation can energy-efficiently provide thermal comfort and good quality of inhaled air. Previous studies were mainly focused on stratum ventilation for cooling, with little attention on stratum ventilation for heating. Proper operation of stratum ventilation for heating is more complicated, with multiple ventilation operation parameters (i.e., the supply vane angle, supply airflow rate, and supply air temperature) non-linearly affecting multiple mutually conflicting criteria of the ventilation performance (i.e., thermal comfort, air quality, and energy efficiency). In this study, the operation of stratum ventilation for heating is optimized by using a multi-criteria decision-making technology, namely a technique of order preference by similarity to ideal solution (TOPSIS). An experiment on stratum ventilation for heating a typical office is conducted. Based on experimentally validated Computational Fluid Dynamics (CFD) simulations, response surface models of the ventilation performance are developed to promote the computational efficiency of the TOPSIS optimization. The operation optimization on the ventilation parameters significantly improves the ventilation performance, with an average reduction of 16.3% and 29.5% in the Predicted Percentage Dissatisfied and the local mean age of air, respectively, and an average increase of 7.0% in the energy utilization coefficient. When the outdoor weather varies, the optimal supply air temperature varies most frequently with the greatest variation range, followed by the optimal supply vane angle, and the optimal supply airflow rate. With the ability to modulate the optimal supply air temperature as a response to outdoor-weather variations, the constant-air-volume system is preferable to the variable-air-volume system.

Occupant feedback based model predictive control for thermal comfort and energy optimization: A chamber experimental evaluation

In current centralized building climate control, occupants do not have much opportunity to intervene the automated control system. This study explores the benefit of using thermal comfort feedback from occupants in the model predictive control (MPC) design based on a novel dynamic thermal sensation (DTS) model. This DTS model based MPC was evaluated in chamber experiments. A hierarchical structure for thermal control was adopted in the chamber experiments. At the high level, an MPC controller calculates the optimal supply air temperature of the chamber heating, ventilation, and air conditioning (HVAC) system, using the feedback of occupants’ votes on thermal sensation. At the low level, the actual supply air temperature is controlled by the chiller/heater using a PI control to achieve the optimal set point. This DTS-based MPC was also compared to an MPC designed based on the Predicted Mean Vote (PMV) model for thermal sensation. The experiment results demonstrated that the DTS-based MPC using occupant feedback allows significant energy saving while maintaining occupant thermal comfort compared to the PMV-based MPC.

The method of evaluating operation performance of HVAC system based on exergy analysis

Abstract A simulation model of an airport terminal's the heating, ventilating and air conditioning (HVAC) system is established. Based on simulation calculation, the unit exergy cost, the object exergy efficiency and the exergy loss ratio of each component of HVAC system are obtained by the structural theory of thermoeconomics. The operation performance of HVAC system and the optimum potential of each component are analyzed by those indexes. In order to evaluate the influence of the control strategy to operation efficiency of HVAC system, a new evaluation method is presented which setting ideal operating level as the criterion, using control perfect index ( CPI ) to evaluate operation performance of the HVAC system. And the energy flow process of this HVAC system is divided into two subsystem energy flow processes (the subsystem A and subsystem B ). Evaluation results show that the annual CPI and the optimum potential of the system are about 0.77 and 23%, respectively, when the original strategy is used. The optimum potential of the subsystem A is larger than the subsystem B . Two control strategies, the optimal load allocation control strategy ( Load Allocation Strategy ) and the optimal supply air temperature reset strategy ( SAT Strategy ) are presented and analyzed by this new evaluation method. Simulation calculation results show that the annual CPI of the system with the Load Allocation Strategy and SAT Strategy are about 0.82 and 0.78, respectively. The Load Allocation Strategy can improve the operation performance of the system more greatly than the SAT Strategy . It means that the evaluation method not only can evaluate the operation performance of the HVAC system, but also can give the direction for optimal control of the HVAC system.

Air handling unit supply air temperature optimal control during economizer cycles

Most air handling units (AHUs) in commercial buildings have an air economizer cycle for free cooling under certain outside air conditions. During the economizer cycle, the outside air and return air dampers are modulated to seek supply air temperature at its setpoint. The supply air temperature is typically set at 55F (13°C) to control humidity in the space. However, dehumidification is not necessary when the outside air is dry. Meanwhile, the space may have less cooling load due to envelope heat loss and/or occupant schedule changes. These facts provide an opportunity to use higher supply air temperature to reduce or eliminate mechanical cooling and terminal box reheat. On the contrary, a higher supply air temperature requires increased air flow as well as fan power. Therefore, an optimization question is formed, through which an optimal supply air temperature is identified to minimize total energy consumption. In this paper a steady-state energy consumption model is established for AHU systems under the economizer, and then an analytical optimization method is used to seek an optimal supply air temperature setpoint to minimize the energy cost. This paper presents AHU system energy modeling, supply air temperature optimization, simulated energy savings, and control sequence development.

Energy-saving strategies with personalized ventilation in cold climates

In this study the influence of the personalized supply air temperature control strategy on energy consumption and the energy-saving potentials of a personalized ventilation system have been investigated by means of simulations with IDA-ICE software. GenOpt software was used to determine the optimal supply air temperature. The simulated office room was located in a cold climate. The results reveal that the supply air temperature control strategy has a marked influence on energy consumption. The energy consumption with personalized ventilation may increase substantially (in the range: 61–268%) compared to mixing ventilation alone if energy-saving strategies are not applied. The results show that the best supply air temperature control strategy is to provide air constantly at 20 °C. The most effective way of saving energy with personalized ventilation is to extend the upper room operative temperature limit (saving up to 60% compared to the reference case). However, this energy-saving strategy can be recommended only in a working environment where the occupants spend most of their time at their workstation. Reducing the airflow rate does not always imply a reduction of energy consumption. Supplying the personalized air only when the occupant is at the desk is not an effective energy-saving strategy.

Optimal supply air temperature with respect to energy use in a variable air volume system

In a variable air volume (VAV) system with 100% outdoor air, the cooling need in the building is satisfied with a certain air flow at a certain supply air temperature. To minimize the system energy use, an optimal supply air temperature can be set dependent on the load, specific fan power (SFP), chiller coefficient of performance, outdoor temperature and the outdoor relative humidity. The theory for an optimal supply air temperature is presented and the heating, ventilation and air-conditioning (HVAC) energy use is calculated depending on supply air temperature control strategy, average U-value of the building envelope and two outdoor climates. The analyses show that controlling the supply air temperature optimally results in a significantly lower HVAC energy use than with a constant supply air temperature. The optimal average U-value of the building envelope is in practise mostly zero.