Search Results (5)

Buildings account for over 40% of global carbon dioxide (CO₂) emissions, with supply and return fans in air handling units consuming a significant portion of energy. To address this, researchers have explored innovative economizer damper control methods and identified the “split-signal” strategy, which optimizes supply airflow using a single damper as a promising approach. In this study, split-signal was further refined for practical application and energy simulation, aiming to demonstrate its effectiveness and encourage adoption in real-world building mechanical systems. Laboratory testing on chilled water variable air volume (VAV) system showed fan energy savings of 0.2–5% compared to traditional “three-coupled” control, depending on ventilation air proportions, and prevented reverse airflow. A statistical regression model, based on experimental data, was developed to predict energy savings and streamline comparisons. Energy simulations were conducted across various U.S. climate zones and revealed potential savings of 15–20% in energy use, operational costs, and CO₂ emissions. With minimal financial investment, split-signal control offers a cost-effective solution to improve energy efficiency and reduce environmental impact, promoting its adoption in real-world building applications. Full article

Buildings account for a large portion of the total energy use in the US; therefore, improving the operation of typical variable-air-volume (VAV) systems in buildings can provide a tremendous economic opportunity. ASHRAE Guideline 36 recommends a resetting strategy for supply air temperature (SAT) for VAV systems based on outside air temperature. However, this strategy may not produce optimal performance, particularly when simultaneous cooling and heating occurs in zones. In addition, there is no strategy recommended in the Guideline to reset the zone minimum airflow set point in a single-duct VAV terminal unit with reheat, although this setpoint has a great impact on zone reheat requirements and ventilation efficiency. Thus, this paper introduces new strategies to reset both the SAT and zone minimum airflow rate set points to improve the efficiency of typical VAV systems. The strategies were tested under various conditions through experiments performed in fully instrumented VAV systems located in the HVAC lab at the University of Cincinnati. The experiments were conducted on a chilled-water VAV system that serves three controlled zones with hot-water reheat VAV boxes controlled by a typical commercial BACnet web-based building automation system BAS. The simulation studies were performed using the building energy simulation software EnergyPlus to evaluate the strategies at a larger scale in various locations. The simulation results show that the proposed resetting strategies can provide fan energy savings between 1.6% and 5.7% and heating load savings between 7.7% to 33.7%, depending on the location. The laboratory testing shows that the proposed strategies can provide stable control performance in actual systems as well as achieving the anticipated reheat and fan energy savings. The result offers significant improvements that can be implemented in the Guideline for single-duct VAV system operation and control. Full article

According to EIA, the Heating Ventilation and Air Conditioning (HAVC) systems account for about 25% of the U.S.’s total commercial building’s energy use. Therefore, advanced modeling and optimization methods of the system components and operation offer great ways to reduce energy consumption in all types of buildings and mainly commercial buildings. This research introduced an innovative integrated two-level optimization technique for the HVAC system to reduce the total energy consumption while improving the indoor thermal comfort level. The process uses actual system performance data collected for the building automation systems (BAS) to create accurate component modeling and optimization process as the first level of optimization (MLO). Artificial neural networks were chosen to be the tool used to serve the process of modeling. The second optimization level utilizes the whole system-level optimization technique (SLO) using a genetic algorithm (G.A.). The proposed two-levels optimization technique will optimize the system setpoints, the supply air temperature, duct static pressure, minimum zone air flowrates, and minimum outdoor air ventilation rate. The proposed technique has contributed to the field of modeling and optimization of HVAC systems through several new contributions. (1) Implementing the demand control methodology with the optimization process to modify the electricity consumption power profile when the demand signal is received. (2) Implement the occupancy schedule inputs into the optimization process to adjust the ventilation airflow rates accordingly. (3) Implement the real-time zone occupancy sensor readings and adjust the zone’s ventilation flowrates and minimum flowrates. (4) Lastly, implementing the method of zone minimum air flowrates setpoint rests to reduce reheat requirements. The proposed optimization process was tested and validated, resulting in savings in the total energy consumed by the chilled water VAV system by 13.4%, 22.4 %, followed by 31% for July, February, and October, respectively. Full article

Most of the energy consumed by the residential and commercial buildings in the U.S. is dedicated to space cooling and heating systems, according to the U.S. Energy Information Administration. Therefore, the need for better operation mechanisms of those existing systems become more crucial. The most vital factor for that is the need for accurate models that can accurately predict the system component performance. Therefore, this paper’s primary goal is to develop a new accurate data-driven modeling and optimization technique that can accurately predict the performance of the selected system components. Several data-enabled modeling techniques such as artificial neural networks (ANN), support vector machine (SVM), and aggregated bootstrapping (BSA) are investigated, and model improvements through model structure optimization proposed. The optimization algorithm will determine the optimal model structures and automate the process of the parametric study. The optimization problem is solved using a genetic algorithm (GA) to reduce the error between the simulated and actual data for the testing period. The models predicted the performance of the chilled water variable air volume (VAV) system’s main components of cooling coil and fan power as a function of multiple inputs. Additionally, the packaged DX system compressor modeled, and the compressor power was predicted. The testing results held a low coefficient of variation (CV%) values of 1.22% for the cooling coil, and for the fan model, it was found to be 9.04%. The testing results showed that the proposed modeling and optimization technique could accurately predict the system components’ performance. Full article

We did energy efficient retrofits for the Indiana University Purdue University—Indianapolis Health Science Building using the eQuest energy software. The current dual-fan dual-duct (DFDD) system is 41 years old and has a higher energy utilization index (EUI) than the national average for similar building types. The baseline model with the DFDD system was compared with the actual electrical consumption. Then, two energy efficiency measures (EEMs) were applied to the model. The first EEM was ‘DFDD system with chilled water and steam heating,’ and the second EEM was ‘single-duct variable air volume (VAV) with chilled water and electric reheat.’ After comparative simulations and analyses, it was determined that the ‘single duct VAV with chilled water and electric reheat’ was the most energy efficient and saved 28% in utility costs. The recommendation given to the facility services was to change the current DFDD system to the single-duct VAV system. The single-duct VAV system will save energy and create additional space above the ceiling after the heating duct is removed. Full article