Context-Aware User Association for Energy Cost Saving in a Green Heterogeneous Network with Hybrid Energy Supplies

2015 ◽  
Vol 20 (6) ◽  
pp. 802-816 ◽  
Author(s):  
Bang Wang ◽  
Qiao Kong ◽  
Laurence T. Yang
Keyword(s):  
Cost Saving ◽  
Energy Cost ◽  
Heterogeneous Network ◽  
User Association ◽  
Context Aware ◽  
Hybrid Energy

scholarly journals Context-aware and priority-based user association and resource allocation in heterogeneous wireless networks

2019 ◽  
Vol 149 ◽  
pp. 76-92 ◽  
Author(s):  
Mohamad Zalghout ◽  
Ayman Khalil ◽  
Matthieu Crussiére ◽  
Samih Abdul-Nabi ◽  
Jean-Francois Hélard
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Heterogeneous Wireless Networks ◽  
User Association ◽  
Context Aware

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 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.

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

Optimized Energy Recovery in Line With Balancing of an ATES

2014 ◽  
Author(s):  
Mohammadreza Behi ◽  
Seyed Aliakbar Mirmohammadi ◽  
Alexander B. Suma ◽  
Björn E. Palm
Keyword(s):  
Energy Storage ◽  
Natural Gas ◽  
Cost Saving ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Energy Cost ◽  
Energy Recovery ◽  
International Energy Agency ◽  
Imbalance Problem ◽  
Aquifer Thermal Energy Storage

The present study explores the potential imbalance problem of the Aquifer Thermal Energy Storage (ATES) system at the Eindhoven University of Technology (TU/e) campus, Eindhoven. This ATES is one of the largest European aquifer thermal energy storage systems, and has a seasonal imbalance problem. Reasons for this issue may be the high cooling demand from laboratories, office buildings and the direct ATES cooling system. Annually, cooling towers use on average 250 MWh electricity for the removal of about 5 GWh of excess heat from the ATES to the surroundings. In addition, the TU/e uses a large amount of natural gas for heating purposes and especially for peak supplies. Recovering the surplus heat of the ATES, a CO2 Trans-critical Heat Pump (HP) system to cover particularly peak demands and total heating demand is proposed, modeled and optimized. The model is validated using data from International Energy Agency. Based on simulation results, 708294 nm3 of natural gas are saved where two different scenarios were considered for the ATES efficiency, cost saving and green house gas reduction. In scenario I, the COP of the ATES increased up to 50% by which K€ 303.3 energy cost and 1288.5 ton CO2 are saved annually. On the other hand, it will be shown that the ATES COP in Scenario II will improve up to 20%. In addition, the proposed energy recovery system results in a 606 ton CO2 -reduction and K€152.7 energy cost saving for the university each year.

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