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 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.

Discriminating market segments using preferential green shift: a conjoint approach

foresight ◽  
2017 ◽  
Vol 19 (4) ◽  
pp. 386-408 ◽  
Author(s):  
Kushagra Kulshreshtha ◽  
Vikas Tripathi ◽  
Naval Bajpai ◽  
Prince Dubey
Keyword(s):  
Cost Saving ◽  
Technology Acceptance ◽  
Energy Cost ◽  
Sampling Technique ◽  
Future Research ◽  
Content Type ◽  
Empirical Generalization ◽  
Probabilistic Sampling ◽  
Model Study ◽  
Consumer Durable

Purpose This paper aims to explore surprising facets of consumer delight behavior. The study is the empirical juncture of three studies based on consumer survey on the Indian television market. Study 1 traces the existence of greenies in India among brownies prevailing around the globe by using the surprise-delight model. Study 2 is a pre-intervention research design confirming greenies preferences to television attributes such as screen technology, annual energy cost saving, screen resolution, screen size and free gifts. Study 3 signifies a price intervention design by allowing customers to include their preference by replacing the annual energy cost saving with price. Design/methodology/approach This paper is a harvest of studies based on discriminant analysis for identifying green and brown customers and a two-level conjoint analysis for identifying attributes contributing to green behavior. Findings The empirical generalization of a study comes out with unique findings of the greenies and brownies and their preference and attitude toward green attribution and substitution. A “preferential green shift” appeared as a vital output owing to knowledge–attitude–practice from these consecutive studies. This gap exists because of the price factor. The authors suggest the measures for improvement in product offering by targeting and positioning green products from the findings and the preferential green shift. Research limitations/implications Future research may focus on other segments of products such as automobiles, i.e. cars. Despite the availability of the non-probabilistic sampling technique, the probabilistic sampling technique can be used. Finally, a larger sample size could have given a better generalization of results. Originality/value The gap in knowledge–attitude–practice was evident. This gap was caused by the presence of “price” concern. The study revealed that heavy consumer durable buyers are aware of the benefit of green, but the reality of price cannot be ignored and finally make a purchasing decision on the basis of price criteria. Hence price is recommended as another criterion to be considered in the technology acceptance models.

scholarly journals Time-Constrained Nature-Inspired Optimization Algorithms for an Efficient Energy Management System in Smart Homes and Buildings

2019 ◽  
Vol 9 (4) ◽  
pp. 792 ◽  
Author(s):  
Ibrar Ullah ◽  
Sajjad Hussain
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Energy Cost ◽  
Management System ◽  
Heuristic Algorithms ◽  
Renewable Energy Sources ◽  
Smart Homes ◽  
Energy Management System ◽  
End User ◽  
Residential Sector

This paper proposes two bio-inspired heuristic algorithms, the Moth-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction, minimization of the Peak to Average power Ratio (PAR) and end-user discomfort minimization are analysed and discussed. Then, a hybrid version of GA and MFO, named TG-MFO (Time-constrained Genetic-Moth Flame Optimization), is proposed for achieving the aforementioned objectives. TG-MFO not only hybridizes GA and MFO, but also incorporates time constraints for each appliance to achieve maximum end-user comfort. Different algorithms have been proposed in the literature for energy optimization. However, they have increased end-user frustration in terms of increased waiting time for home appliances to be switched ON. The proposed TG-MFO algorithm is specially designed for nearly-zero end-user discomfort due to scheduling of appliances, keeping in view the timespan of individual appliances. Renewable energy sources and battery storage units are also integrated for achieving maximum end-user benefits. For comparison, five bio-inspired heuristic algorithms, i.e., Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search Algorithm (CSA), Firefly Algorithm (FA) and Moth-Flame Optimization (MFO), are used to achieve the aforementioned objectives in the residential sector in comparison with TG-MFO. The simulations through MATLAB show that our proposed algorithm has reduced the energy cost up to 32.25% for a single user and 49.96% for thirty users in a residential sector compared to unscheduled load.

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.

A method of assessing the energy cost saving from using an effective door closer

2016 ◽  
Vol 118 ◽  
pp. 329-338 ◽  
Author(s):  
Chun Chen ◽  
Xinye Zhang ◽  
Eckhard Groll ◽  
Aaron McKibben ◽  
Nicholas Long ◽  
...  
Keyword(s):  
Cost Saving ◽  
Energy Cost

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.

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