scholarly journals Evaluating the Upper Bound of Energy Cost Saving by Proactive Data Center Management

2020 ◽  
Vol 17 (3) ◽  
pp. 1527-1541 ◽  
Author(s):  
Ruben Milocco ◽  
Pascale Minet ◽  
Eric Renault ◽  
Selma Boumerdassi
Keyword(s):  
Cost Saving ◽  
Upper Bound ◽  
Data Center ◽  
Energy Cost ◽  
Data Center Management

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.

PPHA-Popularity Prediction Based High Data Availability for Multimedia Data Center

2019 ◽  
Vol 11 (1) ◽  
pp. 17-29
Author(s):  
Kuo-Chi Fang ◽  
Husnu S. Narman ◽  
Ibrahim Hussein Mwinyi ◽  
Wook-Sung Yoo
Keyword(s):  
Data Center ◽  
Service Providers ◽  
Data Access ◽  
Current Data ◽  
Multimedia Data ◽  
Data Availability ◽  
Full Data ◽  
Data Center Management ◽  
Popularity Prediction ◽  
Data Objects

Due to the growth of internet-connected devices and extensive data analysis applications in recent years, cloud computing systems are largely utilized. Because of high utilization of cloud storage systems, the demand for data center management has been increased. There are several crucial requirements of data center management, such as increase data availability, enhance durability, and decrease latency. In previous works, a replication technique is mostly used to answer those needs according to consistency requirements. However, most of the works consider full data, popular data, and geo-distance-based replications by considering storage and replication cost. Moreover, the previous data popularity based-techniques rely on the historical and current data access frequencies for replication. In this article, the authors approach this problem from a distinct aspect while developing replication techniques for a multimedia data center management system which can dynamically adapt servers of a data center by considering popularity prediction in each data access location. Therefore, they first label data objects from one to ten to track access frequencies of data objects. Then, they use those data access frequencies from each location to predict the future access frequencies of data objects to determine the replication levels and locations to replicate the data objects, and store the related data objects to close storage servers. To show the efficiency of the proposed methods, the authors conduct an extensive simulation by using real data. The results show that the proposed method has an advantage over the previous works in terms of data availability and increases the data availability up to 50%. The proposed method and related analysis can assist multimedia service providers to enhance their service qualities.

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