Supply Air Temperature Set Point Research Articles

Field test of active night cooling supplied by district cooling in three commercial buildings

Several previous studies have investigated active night cooling strategies to reduce the peak cooling load in buildings, primarily by precooling the building by the ventilation air during the night. In this study, active night cooling is supplied by the use of district cooling, mainly for cooling buildings by hydronic cooling systems, such as chilled beam or fan coil systems, but potentially also to cool the ventilation air if the outdoor air temperature is above the supply air temperature setpoint. A field test with active night cooling by district cooling was conducted during the summer of 2020 in three commercial buildings located in Gothenburg, Sweden. The active night cooling strategy was implemented by changing the time schedule operation of the buildings’ chilled beam systems to twenty-four hours per day for the first half of the summer and changing back to regular time schedules for the second half. The results showed that active night cooling failed to reduce the hourly maximum cooling power. However, the peak cooling load, corresponding to the 100 hours with the highest cooling power, was reduced with 6.5% for one of the buildings. Active night cooling also reduced the daytime energy usage between 0.7 and 4.6%. The field test demonstrated that some buildings and associated cooling systems are more compatible for active night cooling than others. The test also showed it was possible to achieve some cooling power and energy reductions with simple measures. However, providing active night cooling supplied by district cooling will cause additional costs for the building owners unless it is incentivized by the district cooling provider.

Multi-sensor information fusion based control for VAV systems using thermal comfort constraints

The conventional control methods of variable air volume (VAV) air conditioning systems usually assume that the indoor air is well mixed, and consider each building zone as one node with homogeneous temperature distribution. The average temperature is subsequently used as the controlled parameter in the VAV cascade control process, which might cause uneven temperature distribution and unsatisfactory thermal comfort. This paper presents a coupled simulation of computational fluid dynamics (CFD) and building energy simulation (BES) for the VAV system in an office building located in Shanghai for the purpose of simulating the building, the VAV control system, and indoor thermal environment simultaneously. An external interface is developed to integrate the CFD and BES models based on quasi-dynamic coupling approach. Based upon the developed co-simulation platform, the novel VAV control method is further proposed by fusing information from multiple sensors. By adding two temperature sensors to constrain the thermal comfort of the occupied zone, the supply air temperature setpoint of the VAV terminal unit can be reset in real time. The novel control method is embedded into the co-simulation platform and compared with the conventional VAV control approach. The results illustrate that the temperature distribution under the proposed method is more uniform. At most times of the typical test day, the air diffusion performance indexes (ADPIs) for the proposed method are above 80%, while the ADPIs for the conventional control method are between 60% and 80%. Due to multi-sensor information fusion, the proposed VAV control approach has better ability to ensure the indoor thermal comfort.

Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms

This research applies a data-driven approach to investigate energy savings of a multi-zone HVAC (heating, ventilating, and air conditioning) system. The predictive models of the HVAC energy consumption and the environment conditions of multiple zones are constructed by data mining algorithms. Two major environment conditions, the room temperature and the relative room humidity, are considered. Two variables of operating the HVAC system, the supply air temperature set point and the supply air static pressure set point, in the predictive models are optimized with respect to minimizing the HVAC energy while maintaining the predefined environment conditions of each zone. A novel heuristic search algorithm, the firefly algorithm, is utilized to solve the data-driven predictive models and derive the optimal settings of two set points under required HVAC operational constraints. The firefly algorithm is compared with the particle swarm optimization and evolutionary strategy to demonstrate its advantages in solving the proposed optimization problem. HVAC energy saving with the proposed data-driven framework is examined in the computational studies. A sensitivity analysis of the potential of energy saving based on different types of environment condition constraints is conducted.

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.

Use of genetic algorithms to develop an adaptive fuzzy logic controller for a cooling coil

An adaptive approach to control a water valve for a cooling coil, called an adaptive fuzzy logic controller (AFLC), is developed and validated in this study. The AFLC calculates the error between the supply air temperature and the supply air temperature set point for air in an air handling unit (AHU) of a heating, ventilating, and air conditioning (HVAC) system and continues to improve the fuzzy controller parameters to minimize the error. The AFLC uses genetic algorithms (GAs) to improve the fuzzy rule matrix and fuzzy membership functions for the AHU in HVAC systems. In this paper, the application of genetic algorithms for developing the AFLC is presented. After a brief background on fuzzy logic controllers and GA theory, the use of GAs is explained. Three methods of modifying the fuzzy rule matrix using the GAs are presented along with simulation and real-time experimental results. Experimental results indicate that GAs can be successfully applied to modify an AFLC rule matrix to achieve a better controller.

Choosing the Right Parameter for Single-Duct Constant Air Volume System Supply Air Temperature Reset

To improve system efficiency under part-load conditions, the supply air temperature set point for a single-duct constant air volume air handling unit (AHU) system is often reset based on either return air temperature or outside air temperature. Both reset strategies make engineering sense as long as the reset schedules are reasonable. Quite often the decision to use one over the other is made with the assumption that they will both achieve some energy savings. However, the impact of these two strategies on AHU energy consumption could be very different. An in-depth analysis and comparison of these two commonly used supply air temperature reset strategies for a single-duct constant air volume system is presented in this paper. It is shown that the reset strategy based on outside air temperature is inherently better than that based on return air temperature, with lower energy consumption and improved building comfort level.