Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components

Talib, Rand; Nassif, Nabil; Choi, Wonchang

doi:10.3390/buildings10090163

Cited by 16 publications

(8 citation statements)

References 39 publications

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“…Furthermore, the three modeling tools were trained and tested, and a comparison between the 3 was made to choose the best modeling technique. Lastly, the ANN was selected to be the modeling technique to further model the component of the HVAC system chosen [7,24].…”

Section: Methodsmentioning

confidence: 99%

“…The simulated data or actual performance data can be used to develop data-driven algorithms that can be used for more accurate and flexible predictions than the physical model's estimation data [7]. For example, most of the buildings in the United States are now equipped with BAS (building automation system) to provide us with an outstanding amount of actual building operation data [8].…”

Section: Research Backgroundmentioning

confidence: 99%

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“Demand Control” an Innovative Way of Reducing the HVAC System’s Energy Consumption

Talib¹,

Nassif²

2021

Buildings

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

“Demand Control” an Innovative Way of Reducing the HVAC System’s Energy Consumption

Talib¹,

Nassif²

2021

Buildings

Self Cite

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“…In the Heating, Ventilation, and Air Conditioning (HVAC) system, refined numerical information in its inner structure was utilized in reflecting the adjustment of various parameters, functions, and coefficients. Amongst this, the Proportional Integral Derivative (PID) model was frequently utilized to improve the control rules for generators, exchangers, and distributors, especially, in the field of rule-based thermal control systems [1][2][3].…”

Section: Thermal Systems In Buildingsmentioning

confidence: 99%

Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season

Ahn

2020

Sustainability

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In thermal controls in buildings, recent statistical and data-driven approaches to optimize supply air conditions have been examined in association with several types of building spaces and patterns of energy consumption. However, many strategies may have some problems where high-control precision may increase energy use, or low energy use in systems may decrease indoor thermal quality. This study investigates a neural network algorithm with an adaptive model on how to control the supply air conditions reflecting learned data. During the process, the adaptive model complements the signals from the network to independently maintain the comfort level within setting ranges. Although the proposed model effectively optimizes energy consumption and supply air conditions, it achieves quite improved comfort levels about 14% more efficient than comparison models. Consequently, it is confirmed that a network and learning algorithm equipped with an adaptive controller properly responds to users’ comfort levels and system’s energy consumption in a single space. The improved performance in space levels can be significant in places where many spaces are systematically connected, and in places which require a high consistency of indoor thermal comfort. Another advantage of the proposed model is that it properly reduces an increase in energy consumption despite an intensive strategy is utilized to improve thermal comfort.

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“…With time, researches and improvements were made and the CAV system was changed to a VAV system with a single fan or a dual fan configuration [11,12]. Simulations and modeling techniques for optimized performance of the different system components have also been developed [13][14][15][16] along with fault and anomaly detection in system operation [17]. Thus, advanced control strategies and optimization of the dual duct systems can play a significant role in the pursuit of energy consumption reduction and thermal comfort.…”

Section: Introductionmentioning

confidence: 99%

Improving Building Energy Performance Using Dual VAV Configuration Integrated with Dedicated Outdoor Air System

Nassif

Ridwana

2021

Buildings

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As building systems account for almost half of the total energy consumed by the building sector to provide space heating, cooling, and ventilation, efficiently designing these systems can be the key to energy conservation in buildings. Dual VAV systems with an effective control strategy can substantially reduce the energy consumption in buildings, providing a significant scope of further research on this system configuration. This paper proposes to utilize the warm air duct of the dual VAV system as a dedicated outdoor air (DOA) unit when no heating is required, which allows the cooling load to be effectively distributed between two ducts. A specific control sequence is proposed with different supply air temperature reset strategies to estimate the heating, cooling loads, and fan power energy consumption of the proposed system. A simple two-zone office building is taken as a preliminary case study to simulate the airflow rates and fan power of a single duct VAV and proposed dual VAV systems to illustrate the concept. Finally, a larger multi-zone office building is simulated to measure the annual heating, cooling loads, and fan power energy and compare the energy savings among the systems. The results show significant fan power reduction ranging from 1.7 to 9% and notable heating energy reduction up to 76.5% with a small amount of cooling load reduction varying from 0.76 to 2.56% depending on the different locations for the proposed dual VAV systems. Further energy savings from different supply air temperature reset strategies demonstrate the opportunity of employing them according to climates and case studies. The proposed dual VAV system proves to have the potential to be adapted in buildings for the purpose of sustainability and energy savings.

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Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components

Cited by 16 publications

References 39 publications

“Demand Control” an Innovative Way of Reducing the HVAC System’s Energy Consumption

“Demand Control” an Innovative Way of Reducing the HVAC System’s Energy Consumption

Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season

Improving Building Energy Performance Using Dual VAV Configuration Integrated with Dedicated Outdoor Air System

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