Achieving Energy Cost Saving Using Best Energy Tariff: a Case Study of a Sewage Plant

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.

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Hybrid systems for prediction-a case study of predicting effluent flow to a sewage plant

Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems ◽

10.1109/annes.1995.499485 ◽

2002 ◽

Author(s):

M. Bailey ◽

N. Kasabov ◽

T. Cohen ◽

P. Mason ◽

A. Gray

Keyword(s):

Hybrid Systems ◽

Sewage Plant

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CASE STUDY OF ENERGY CONSERVATION IN INDUSTRY BY APPLICATION OF LEAN TOOLS

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i12.018 ◽

2021 ◽

Vol 5 (12) ◽

Author(s):

N.Sujith Prasanna ◽

Dr.J.Nagesh Kumar

Keyword(s):

Energy Conservation ◽

Energy Saving ◽

Energy Cost ◽

Energy Use ◽

Energy Savings ◽

Sustainable Manufacturing ◽

Integrated Approach ◽

Energy Saving Potential ◽

Lean Tools

Energy cost is significant in many of the manufacturing activities. The efficiency of energy use is quiet low as there are substantial visible and hidden losses. Visible losses can be easily identified and corrective action can be taken. However hidden and indirect losses form a sizeable portion of the losses. Identifying these losses is not easy and requires an integrated approach which includes thorough study of process, operations and their interactions with energy use. Industries across sectors have implemented lean management principles which target various wastes occurring in the plant. This paper discusses case studies which highlight the exploitation of lean tools as a means for unearthing hidden energy saving potential that often go unnoticed. In addition to the energy savings which results in improved profits and competitiveness, the approach also aids the industry to pursue a path of sustainable manufacturing.

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

10.32920/ryerson.14646717 ◽

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.

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

10.32920/ryerson.14646717.v1 ◽

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.

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