EVALUATION OF SMART BOOSTER FANS AND DAMPERS FOR ADVANCED HVAC SYSTEMS

2021 ◽  
Vol 16 (2) ◽  
pp. 115-127
Author(s):  
Behdad Rezanejadzanjani ◽  
Paul G. O’Brien
Keyword(s):  
Numerical Analysis ◽  
Numerical Evaluation ◽  
Winter Season ◽  
Hvac Systems ◽  
Hvac System ◽  
Duct System ◽  
Running Time ◽  
Booster Fans ◽  
System 1 ◽  
Booster Fan

ABSTRACT There is potential to significantly reduce CO2 emissions by increasing the efficiency and reducing the duty cycle of HVAC systems by using smart booster fans and dampers. Smart booster fans fit in the vents within a home, operating quietly on low power (2W) to augment HVAC systems and improve their performance. In this study, a prototype duct system is used to measure and evaluate the ability for smart booster fans and dampers to control airflow to different vents for the purpose of increasing the efficiency of HVAC systems. Four case studies were evaluated: an HVAC system (1) without any fans or dampers, (2) with a fan installed in one vent, but without any dampers, (3) with dampers installed at the vents, but without any fans, and (4) with both fan and dampers installed. The results from both the experimental and numerical evaluation show that the smart booster fan and dampers can significantly improve the airflow at a vent that is underperforming. For example, the airflow at the last vent in a ducting branch was increased from 17 to 37 CFM when a smart booster fan was installed at this vent. Results from the numerical analysis show that for the case of an underperforming vent during the winter season the HVAC running time may be reduced from 24 hr/day to 5.6 hr/day. Furthermore, results from the numerical analysis show the HVAC running time is further reduced to 4.5 hr/day for cases 3 and 4.

scholarly journals A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 400 ◽  
Author(s):  
Zelin Nie ◽  
Feng Gao ◽  
Chao-Bo Yan
Keyword(s):  
Optimization Model ◽  
Air Conditioning ◽  
State Of The Art ◽  
Hvac Systems ◽  
Practical Significance ◽  
Hvac System ◽  
Bilinear Model ◽  
Optimization And Control ◽  
And Control ◽  
Slow Timescale

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

scholarly journals Operational optimization of residential HVAC system using model predictive control strategy planning

2021 ◽  
Author(s):  
Nima Alibabaei ◽  
Alan S. Fung
Keyword(s):  
Cost Saving ◽  
Energy Cost ◽  
Carbon Dioxide Emissions ◽  
Hvac Systems ◽  
Conservation Strategy ◽  
Hvac System ◽  
Heating Season ◽  
Residential Sector ◽  
Simulation Engine ◽  
Strategy Planning

To date, the residential sector accounts for a major portion of consumption by consuming more than 40% of the entire world's energy and producing 33% of the carbon dioxide emissions. In North America, the residential sector energy consumptions are mainly related to heating, ventilation, and air conditioning (HVAC) systems, which are not operating in the most efficient ways due to existing on/off and conventional controllers. In Ontario, due to the variable price of electricity, variation in outdoor disturbances, and new Ontario Government sweeping mandate in overhauling the energy use in residential sector, there is an opportunity to develop intelligent control systems to employ energy conservation strategy planning model (ECSPM) in existing HVAC systems for reducing their operating cost, energy consumption, and GHG emission. In order to take advantage of these opportunities, two model-based predictive controllers (MPCs) were developed in this Ph.D. research. In the first MPC controller, a Matlab-TRNSYS co-simulator was developed to fill the lack of advanced controllers in building energy simulators. This cosimulator investigated the effectiveness of different novel ECSPMs on an HVAC system's energy cost saving during winter and summer seasons. This co-simulator offered 23.8% saving in the HVAC system's energy costs in the heating season. Regardless of the strong capabilities, employing this co-simulator for implementing comprehensive/complex optimization methods resulted in an unacceptably long optimization time due to the of TRNSYS simulation engine. Therefore, in the second PMC controller, simplified house thermal and HVAC system models were developed in Matlab. To design a grid-friendly house, this model was enhanced by integrating on-site renewable energy generation and storage systems. A novel algorithm was developed to reduce the MPC controller optimization time. The effectiveness of the novel MPC model in the HVAC system's energy cost saving was compared with a Simple Rule-based (SRB) controller, which itself is an efficient HVAC controller, while this controller offered 12.28% additional savings in the heating season.

scholarly journals Operational optimization of residential HVAC system using model predictive control strategy planning

2021 ◽  
Author(s):  
Nima Alibabaei ◽  
Alan S. Fung
Keyword(s):  
Cost Saving ◽  
Energy Cost ◽  
Carbon Dioxide Emissions ◽  
Hvac Systems ◽  
Conservation Strategy ◽  
Hvac System ◽  
Heating Season ◽  
Residential Sector ◽  
Simulation Engine ◽  
Strategy Planning

To date, the residential sector accounts for a major portion of consumption by consuming more than 40% of the entire world's energy and producing 33% of the carbon dioxide emissions. In North America, the residential sector energy consumptions are mainly related to heating, ventilation, and air conditioning (HVAC) systems, which are not operating in the most efficient ways due to existing on/off and conventional controllers. In Ontario, due to the variable price of electricity, variation in outdoor disturbances, and new Ontario Government sweeping mandate in overhauling the energy use in residential sector, there is an opportunity to develop intelligent control systems to employ energy conservation strategy planning model (ECSPM) in existing HVAC systems for reducing their operating cost, energy consumption, and GHG emission. In order to take advantage of these opportunities, two model-based predictive controllers (MPCs) were developed in this Ph.D. research. In the first MPC controller, a Matlab-TRNSYS co-simulator was developed to fill the lack of advanced controllers in building energy simulators. This cosimulator investigated the effectiveness of different novel ECSPMs on an HVAC system's energy cost saving during winter and summer seasons. This co-simulator offered 23.8% saving in the HVAC system's energy costs in the heating season. Regardless of the strong capabilities, employing this co-simulator for implementing comprehensive/complex optimization methods resulted in an unacceptably long optimization time due to the of TRNSYS simulation engine. Therefore, in the second PMC controller, simplified house thermal and HVAC system models were developed in Matlab. To design a grid-friendly house, this model was enhanced by integrating on-site renewable energy generation and storage systems. A novel algorithm was developed to reduce the MPC controller optimization time. The effectiveness of the novel MPC model in the HVAC system's energy cost saving was compared with a Simple Rule-based (SRB) controller, which itself is an efficient HVAC controller, while this controller offered 12.28% additional savings in the heating season.

scholarly journals Fault detection in HVAC systems using a distribution considering uncertainties

2019 ◽  
Vol 111 ◽  
pp. 05010
Author(s):  
Shohei Miyata ◽  
Yasunori Akashi ◽  
Jongyeon Lim ◽  
Yasuhiro Kuwahara
Keyword(s):  
Fault Detection ◽  
Performance Indicator ◽  
High Energy ◽  
Hvac Systems ◽  
Coefficient Of Performance ◽  
Total Power ◽  
Hvac System ◽  
Total Power Consumption ◽  
Detailed Simulation ◽  
Performance Distribution

Detecting and diagnosing faults that degrade the performance of heating, ventilation, and air conditioning (HVAC) systems is very important for maintaining high energy efficiency. The performance of HVAC systems can be evaluated by analyzing monitored data. However, data from a HVAC system generally includes uncertainties, which renders monitored data less reliable. Then, we focused on uncertainties and a calculated performance distribution. The uncertainties from sensors, actuators, and communications were modelled stochastically and were incorporated into a detailed simulation. The system coefficient of performance (SCOP) was used as a performance indicator, which is defined as the ratio of suppled heat to total power consumption. The SCOP distributions over the course of representative weeks in 2007 and 2015 were calculated by repeating the simulation 2,000 times with different uncertainties. Regarding the results for 2015, the 90% confidence interval of the distribution was -4.9% to 5.8% from the SCOP value without uncertainties. The SCOP value determined from the monitored data in 2015 was outside of the low end of the distribution though that in 2007 was inside of the interval. Through an analysis of the monitored data, it was found that fault detection is possible by comparing the monitored data with the distribution.

Performance Analysis of Two HVAC Systems for Zero Energy Research Laboratory, Denton, TX

2013 ◽  
Author(s):  
Naimee Hasib ◽  
Junghyon Mun ◽  
Yong X. Tao
Keyword(s):  
Real Data ◽  
Hvac Systems ◽  
Simulation Software ◽  
Hvac System ◽  
Zero Energy ◽  
North Texas ◽  
Human Comfort ◽  
Air Conditioning System ◽  
Energy Plus ◽  
Calibrated Simulation

HVAC (Heating, Ventilation & Air Conditioning) system is the most significant part of a building which directly associated with human comfort. Modern HVAC system optimizes all the parameters like temperature, humidity and indoor air quality to give the occupant the best comfort. Beside human comfort some other crucial factors like installation, maintenance & operational cost, efficiency, availability and controlling method of the system need to be taken into consideration. This paper covers the study and comparison among two different HVAC systems to achieve the goal of finding the better effective HVAC system in terms of human comfort, efficiency considering North Texas climate. In this paper; power consumption, human comfort & efficiency analysis is done for the existing WWHP & WAHP system (in UNT ZØE) using Energy Plus simulation software. Calibration of the simulation data of the existing system is done comparing with the real data. After the baseline model is calibrated, simulation for other HVAC systems like evaporative cooler (EC) is conducted. The comparison analysis of both the HVAC systems shows the better effective HVAC system in North Texas weather considering all the relevant issues and challenges. The result will make UNT Zero Energy lab more energy efficient and a standard model towards a sustainable green future.

Review and Evaluation of Using R-C Thermal Modeling of Cooling Load Prediction for HVAC System Control Purpose

2012 ◽  
Author(s):  
Oluwaseyi T. Ogunsola ◽  
Li Song
Keyword(s):  
Thermal Modeling ◽  
Model Performance ◽  
Hvac Systems ◽  
Measured Data ◽  
System Control ◽  
Cooling Load ◽  
Hvac System ◽  
Monitoring And Control ◽  
Load Prediction ◽  
Total Energy Consumption

Heating and cooling loads which are compensated by heating, ventilation, and air-conditioning (HVAC) systems, are the main reason for energy uses in buildings. Energy utilized by HVAC system accounts for two-thirds of a building’s total energy consumption. Excessive energy is consumed when HVAC systems fail to operate as intended. This is often due to several factors such as inappropriate monitoring and control strategy, lack of understanding of the dynamics of thermal loads, and system complexity. Amidst several models, estimation of cooling load using Resistance Capacitance (RC) models have proved to provide more robust and accurate estimates of the building load based on measured data but the use of this method is not without challenges. This study aims to highlight common challenges associated with implementation of the RC method for thermal modeling of cooling load. Past and current research have handled some of the challenges by introducing simplifying assumptions which if not adequately selected can lead to significant deviation between model performance and measured data. Without proper understanding of the challenges, engineers may not be able to place a high degree of confidence in load calculation methods and the computer implementations that they use.

The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

2016 ◽  
Vol 120 ◽  
pp. 145-158 ◽  
Author(s):  
Antonio E. Ruano ◽  
Shabnam Pesteh ◽  
Sergio Silva ◽  
Helder Duarte ◽  
Gonçalo Mestre ◽  
...  
Keyword(s):  
Hvac Systems ◽  
Hvac System ◽  
System A

scholarly journals CFD Analysis of Auditorium using Ansys Fluent

2019 ◽  
Vol 8 (2) ◽  
pp. 4533-4538
Keyword(s):  
Air Conditioning ◽  
Fluid Dynamic ◽  
Winter Season ◽  
Building Design ◽  
Project Work ◽  
Outlet Temperature ◽  
Hvac System ◽  
Computational Fluid Dynamic Analysis ◽  
Ansys Fluent ◽  
Conditioning Equipment

The primary aspect of any building design and management is heating, ventilation and air conditioning (HVAC). Such systems play very important role in building construction and then the comfort of the occupants of buildings. Hence proper design of such HVAC system is necessary and is essential for efficient and green buildings the HVAC equipment perform the duty of heating and/ or cooling for residential and commercial buildings. Such HVAC system also provide fresh outdoor air to dilute the air contaminants such as odor from occupants of buildings, volatile organic compounds , chemicals etc. Air conditioning equipment is one of the major components in HVAC system. In the project work, an effort has been made to analyses the HVAC system used in seminar halls of which have sitting capacity of 100 people. It is very much essential to have comfortless for people participating in events like seminar, conferences, commercial presentations in seminar hall. Good cooling of seminar hall is essential especially in summer season and moderate warmness is necessary in winter season. In sitting arrangements, the 10 chairs are arranged in 10 rows. The Computational Fluid Dynamic analysis of HVAC system available in seminar hall is carried out by using ANSYS FLUENT software both summer and winter seasons. Parameter studies have been carried out by varying inlet velocity of air in the range 0.1 to 0.5 m/s. the results have been presented in the form of velocity, pressure and temperature contours. As it is observed that as inlet air velocity increases from 0.1 to 0.5 m/s. the outlet temperature decreases from 307 to 302K.

Intelligent Approaches for Modeling and Optimizing HVAC Systems’ Energy Use

2017 ◽  
Author(s):  
Raymond C. Tesiero ◽  
Nabil Nassif ◽  
Balakrishna Gokaraju ◽  
Daniel Adrian Doss
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Hvac Systems ◽  
Optimization Process ◽  
Hvac System ◽  
Building Automation ◽  
Set Point ◽  
Chilled Water ◽  
Self Tuning ◽  
Self Learning

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This paper evaluates model-based optimization processes (OP) for HVAC systems utilizing any computer algebra system (CAS), genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) is tested using data collected from an actual HVAC system. Using this optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. This research focuses primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort.

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