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.

Optimal Control Strategy for Ice-Storage System Based on Hourly Dynamic Load Simulation

2013 ◽  
Vol 671-674 ◽  
pp. 2515-2519
Author(s):  
Xue Mei Wang ◽  
Zhen Hai Wang ◽  
Xing Long Wu
Keyword(s):  
Optimal Control ◽  
Air Conditioning ◽  
Control Strategies ◽  
Storage System ◽  
Simulation Software ◽  
Ice Storage ◽  
Optimal Control Strategy ◽  
Air Conditioning System ◽  
Energy Plus ◽  
Load Simulation

This project aims to study the optimal control model of the ice-storage system which is theoretically close to the optimal control and also applicable to actual engineering. Using Energy Plus, the energy consumption simulation software, and the simple solution method of optimal control, researchers can analyze and compare the annual operation costs of the ice-storage air-conditioning system of a project in Beijing under different control strategies. Researchers obtained the power rates of the air-conditioning system in the office building under the conditions of chiller-priority and optimal contro1 throughout the cooling season. Through analysis and comparison, they find that after the implementation of optimal control, the annually saved power bills mainly result from non-design conditions, especially in the transitional seasons.

scholarly journals Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2181
Author(s):  
Alberto Garces-Jimenez ◽  
Jose-Manuel Gomez-Pulido ◽  
Nuria Gallego-Salvador ◽  
Alvaro-Jose Garcia-Tejedor
Keyword(s):  
Optimization Problems ◽  
Random Search ◽  
Real Data ◽  
Hvac Systems ◽  
Rate Of Change ◽  
Coefficient Of Performance ◽  
Hvac System ◽  
Nsga Ii ◽  
Transient Time ◽  
Soft Computing Techniques

Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarm-intelligence-based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.

scholarly journals The performance of pip-cascade controler in HVAC system

2014 ◽  
Vol 18 (suppl.1) ◽  
pp. 213-220 ◽  
Author(s):  
Khaled Legweel ◽  
Dragan Lazic ◽  
Milan Ristanovic ◽  
Jasmina Lozanovic-Sajic
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Air Conditioning ◽  
Hvac Systems ◽  
The Other ◽  
Hvac System ◽  
Air Conditioning System ◽  
Central Air Conditioning ◽  
High Level ◽  
The Mathematical Model

Primitive controllers used in the early version for HVAC systems, like the on-off (Bang-Bang) controller, are inefficient, inaccurate, unstable, and suffer from high-level mechanical wear. On the other hand, other controllers like PI and cascade controllers, overcome these disadvantages but when an offset response (inaccurate response) occurs, power consumption will increase. In order to acquire better performance in the central air-conditioning system, PIP-cascade control is investigated in this paper and compared to the traditional PI and PID, in simulation of experimental data. The output of the system is predicted through disturbances. Based on the mathematical model of air-conditioning space, the simulations in this paper have found that the PIP-cascade controller has the capability of self-adapting to system changes and results in faster response and better performance.

scholarly journals Zero Energy Building Economic and Energetic Assessment with Simulated and Real Data Using Photovoltaics and Water Flow Glazing

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3272
Author(s):  
Fernando del Ama Gonzalo ◽  
Belen Moreno Santamaria ◽  
José Antonio Ferrándiz Gea ◽  
Matthew Griffin ◽  
Juan A. Hernandez Ramos
Keyword(s):  
Water Flow ◽  
Energy Savings ◽  
Real Data ◽  
Primary Energy ◽  
Simulation Software ◽  
Photovoltaic System ◽  
Zero Energy ◽  
New Paradigm ◽  
Photovoltaic Array ◽  
Yield Factor

The new paradigm of Net Zero Energy buildings is a challenge for architects and engineers, especially in buildings with large glazing areas. Water Flow Glazing (WFG) is a dynamic façade technology shown to reduce heating and cooling loads for buildings significantly. Photovoltaic panels placed on building roofs can generate enough electricity from solar energy without generating greenhouse gases in operation or taking up other building footprints. This paper investigates the techno-economic viability of a grid-connected solar photovoltaic system combined with water flow glazing. An accurate assessment of the economic and energetic feasibility is carried out through simulation software and on-site tests on an actual prototype. The assessment also includes the analysis of global warming potential reduction. A prototype with WFG envelope has been tested. The WFG prototype actual data reported primary energy savings of 62% and 60% CO2 equivalent emission reduction when comparing WFG to a reference triple glazing. Finally, an economic report of the Photovoltaic array showed the Yield Factor and the Levelized Cost of Energy of the system. Savings over the operating lifetime can compensate for the high initial investment that these two technologies require.

scholarly journals Numerical Evaluation of a HVAC System Based on a High-Performance Heat Transfer Fluid

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3298
Author(s):  
Gianpiero Colangelo ◽  
Brenda Raho ◽  
Marco Milanese ◽  
Arturo de Risi
Keyword(s):  
Heat Transfer ◽  
High Performance ◽  
Cooling System ◽  
Electrical Energy ◽  
Simulation Software ◽  
Heat Transfer Fluid ◽  
Hvac System ◽  
Dynamic Simulations ◽  
Heating And Cooling ◽  
The University

Nanofluids have great potential to improve the heat transfer properties of liquids, as demonstrated by recent studies. This paper presents a novel idea of utilizing nanofluid. It analyzes the performance of a HVAC (Heating Ventilation Air Conditioning) system using a high-performance heat transfer fluid (water-glycol nanofluid with nanoparticles of Al2O3), in the university campus of Lecce, Italy. The work describes the dynamic model of the building and its heating and cooling system, realized through the simulation software TRNSYS 17. The use of heat transfer fluid inseminated by nanoparticles in a real HVAC system is an innovative application that is difficult to find in the scientific literature so far. This work focuses on comparing the efficiency of the system working with a traditional water-glycol mixture with the same system that uses Al2O3-nanofluid. The results obtained by means of the dynamic simulations have confirmed what theoretically assumed, indicating the working conditions of the HVAC system that lead to lower operating costs and higher COP and EER, guaranteeing the optimal conditions of thermo-hygrometric comfort inside the building. Finally, the results showed that the use of a nanofluid based on water-glycol mixture and alumina increases the efficiency about 10% and at the same time reduces the electrical energy consumption of the HVAC system.

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.

Discrete Event Simulation Of The COVID-19 Sample Collection Point Operation

2021 ◽  
Author(s):  
Martina Kuncova ◽  
Katerina Svitkova ◽  
Alena Vackova ◽  
Milena Vankova
Keyword(s):  
Discrete Event Simulation ◽  
Waiting Times ◽  
Discrete Event ◽  
Real Data ◽  
Simulation Software ◽  
Sample Collection ◽  
The Real ◽  
Event Simulation ◽  
Collection Process ◽  
Collection Point

The year 2020 was very challenging for everyone due to the COVID-19 pandemic. Many people turn their lives upside down from day to day. Politicians had to impose completely unprecedented measures, and doctors immediately had to adapt to the huge influx of patients and the massive demand for testing. Of course, not all processes could be planned completely efficiently, given that the situation literally changes from minute to minute, but sometimes better planning could improve the real processes. This contribution deals with the application of simulation software SIMUL8 to the analysis of the COVID-19 sample collection process in a drive-in point in a hospital. The main aim is to create a model based on the real data and then to find out the suitable number of other staff (medics) helping a doctor during the process to decrease the number of unattended patients and their waiting times.

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