scholarly journals A Decision Support Approach to Provide Sustainable Solutions to the Consumer, by Using Electrical Appliances

2019 ◽  
Vol 11 (4) ◽  
pp. 1143 ◽  
Author(s):  
João Matias ◽  
Ricardo Santos ◽  
Antonio Abreu
Keyword(s):  
Energy Efficiency ◽  
Decision Support ◽  
Evolutionary Algorithms ◽  
Co2 Emissions ◽  
Water Consumption ◽  
Optimization Technique ◽  
Relative Importance ◽  
Sustainable Solutions ◽  
Knowing That

The diversity of energy efficiency appliances existent on the market, with all its different issues, contributes to the existence of several tradeoffs (e.g., energy and water consumption vs. initial investment), which make the consumer’s choices in the market difficult. This becomes even more relevant, by knowing that nowadays a consumer tries to get a solution from the market, with a good compromise between the economic, social and environmental dimensions, and according to its priorities and specific needs, which can be different from other consumers. By adopting a multicriteria approach, combined with an optimization technique, based on evolutionary algorithms (EA), it will be possible to provide a set of sustainable solutions from the market to the consumer, that respects the compromise referred before. In this work, it will be presented an approach to support a decision-agent (DA) (consumer), by performing a set of sustainable choices based on electrical appliances, from the market and suitable to its needs. The method will be applied to a case study, to demonstrate its application. Regarding the obtained solutions, several savings are achieved (electrical and water consumption, CO2 emissions) by taking into account the consumer’s relative importance, regarding each dimension considered.

scholarly journals Using Evolutionary Algorithms to Achieve Sustainable Solutions through Household Appliances

2019 ◽  
Vol 11 (2) ◽  
pp. 69-76
Author(s):  
Ricardo Santos ◽  
J. C. O. Matias ◽  
Antonio Abreu
Keyword(s):  
Evolutionary Algorithms ◽  
Co2 Emissions ◽  
Water Consumption ◽  
Optimization Technique ◽  
Relative Importance ◽  
Consumer Decision ◽  
Household Appliances ◽  
Sustainable Solutions ◽  
Good Compromise ◽  
Social Concerns

 In order to reach a sustainable planet, there is a permanent need by the consumer (decision- agent), to achieve sustainable solutions, with its decisions. Given the importance of the buildings, as a sector to achieve such solutions, as well as the diversity of household appliances existent on the market, with all its different issues, there are several tradeoffs to consider (e.g. energy and water consumption vs initial investment), which difficult the consumer’s choices from the market. The problem increases, since nowadays, the consumer tries to get a solution from the market, with a good compromise between the Economic, Social and Environmental dimensions, and according to its specific needs, which can be different from other consumers. By considering a multicriteria approach, combined with an optimization technique, based on Evolutionary Algorithms (EA), it’s provided a set of sustainable solutions from the market to the consumer that respects the compromise referred before. In this work, it is presented an approach to support a decision-agent (DA) (consumer), by performing a set of sustainable choices based on household appliances from the market and suitable to its needs. Based on the obtained solutions, several savings are achieved (electrical and water consumption, CO2 emissions), by considering the consumer’s relative importance, regarding its Economics, Environmental and Social concerns.

scholarly journals A Soft Computing Framework to Support Consumers in Obtaining Sustainable Appliances from the Market

2020 ◽  
Vol 10 (9) ◽  
pp. 3206
Author(s):  
Ricardo Santos ◽  
António Abreu ◽  
José Soares ◽  
Fernanda Mendes ◽  
João M.F. Calado
Keyword(s):  
Evolutionary Algorithms ◽  
Co2 Emissions ◽  
Soft Computing ◽  
Water Consumption ◽  
Economic Sectors ◽  
Household Appliances ◽  
Sustainable Solutions ◽  
Initial Cost ◽  
Computing Framework

Currently, sustainability is considered a priority by society, with the household appliances being one of the economic sectors involved in achieving sustainability. However, the existence of several issues (e.g., energy and water consumption, reliability, initial cost, and illuminance, among others) together with the diversity of brands and models on the market, make the consumer’s decisions regarding sustainable options difficult, according to their concerns and related to each sustainability dimension (economic, environmental, and social). By combining evolutionary algorithms (EA) with multicriteria techniques, it is possible to achieve sustainable solutions for the consumer based on their requirements. In this paper, a method is presented to support the consumer by obtaining a set of sustainable household appliances on the market that suit their preferences, concerns, and needs. By using a case study to apply the approach developed here, a set of sustainable appliances from the market is obtained, where several benefits are achieved (e.g., energy and water consumption savings, avoidance of CO2 emissions) during the lifecycle of each appliance, chosen from the appliance’s industry.

Sensor-based ambient intelligence for optimal energy efficiency

Sensor Review ◽  
2014 ◽  
Vol 34 (2) ◽  
pp. 170-181 ◽  
Author(s):  
David Robinson ◽  
David Adrian Sanders ◽  
Ebrahim Mazharsolook
Keyword(s):  
Energy Efficiency ◽  
Decision Support ◽  
Energy Consumption ◽  
Energy Management ◽  
Research Work ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Content Type ◽  
Energy Management Systems

Purpose – This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation. Design/methodology/approach – A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency. Findings – An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems. Research limitations/implications – The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described). Practical implications – A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved. Originality/value – For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.

On the Design of a Sustainable Production Line: The MetaCAM Tool

2015 ◽  
Author(s):  
Apostolos Fysikopoulos ◽  
Theocharis Alexopoulos ◽  
George Pastras ◽  
Panos Stavropoulos ◽  
Georgios Chryssolouris
Keyword(s):  
Energy Efficiency ◽  
Decision Support ◽  
Value Chain ◽  
Production Line ◽  
Optimization Procedure ◽  
Decision Support Tool ◽  
Life Cycles ◽  
Support Tool ◽  
Industrial Sectors

Nowadays, manufacturing enterprises face enormous environmental challenges, due to complex and diverse economic trends, including shorter product life cycles, rapid advances in science and technology, increased diversity in customer demands and globalization of production activities. Consequently, the cost is highly affected by environmentally related factors. Energy efficiency is one of the main factors, which together with waste management, affect manufacturing decisions. The complexity and diversity of the factors that determine energy efficiency require intelligent systems for their optimization at each “manufacturing level”. Manufacturing decisions should be taken as fast as possible and with the highest possible accuracy. Artificial intelligence/machine learning tools have made significant progress during the last decade and are suitable for such applications. The main objective of the current study is that an architecture for the development of a networked, online, decision support tool, be provided towards achieving sustainable value chain management. The main idea behind the proposed design is that stakeholders be assisted in taking decisions towards improving the energy and eco-efficiency of the entire value chain or parts of it. This is suggested within the context of a multi-objective optimization procedure, taking into account other important decision making attributes, such as flexibility, quality and time for the final reduction in the overall cost. This architecture incorporates real time information modules that interact with online monitoring systems, using any available information within the value chain and the existing IT tools. A partial realization of the proposed idea is implemented in the form of a user friendly software tool (the MetaCAM tool). This based, decision support tool aiming to optimize a current production line or to propose alternatives for the manufacturing of a product. The tool performs optimization based on a set of predefined criteria, namely energy, waste, cost and time. For each of these criteria, the end-user selects the desired weight factor in order to drive the optimization procedure accordingly. The tool presents the characteristics of the setup of the proposed optimized line and maintains all used data and calculations in order to be reused when necessary. For the tool’s validation, three real case studies from different industrial sectors have been used. The first case study comes from the domestic appliances sector (refrigerator door panel), the second one from the automotive sector (a two seat bench for light commercial vehicles) and finally, the third case study derives from the aeronautics sector and deals with the production of the loading ramp hinge of a military aircraft.

scholarly journals How to Prioritize Energy Efficiency Intervention in Municipal Public Buildings to Decrease CO2 Emissions? A Case Study from Italy

2020 ◽  
Vol 17 (12) ◽  
pp. 4434
Author(s):  
Filomena Pietrapertosa ◽  
Marco Tancredi ◽  
Michele Giordano ◽  
Carmelina Cosmi ◽  
Monica Salvia
Keyword(s):  
Energy Efficiency ◽  
Co2 Emissions ◽  
Energy Savings ◽  
Action Plan ◽  
The European Union ◽  
Public Buildings ◽  
Building Stock ◽  
End Use ◽  
Climate Crisis

The European Union 2050 climate neutrality goal and the climate crisis require coordinated efforts to reduce energy consumption in all sectors, and mainly in buildings greatly affected by the increasing temperature, with relevant CO2 emissions due to inefficient end-use technologies. Moreover, the old building stock of most countries requires suited policies to support renovation programs aimed at improving energy performances and optimize energy uses. A toolbox was developed in the framework of the PrioritEE project to provide policy makers and technicians with a wide set of tools to support energy efficiency in Municipal Public Buildings. The toolbox, available for free, was tested in the partners’ communities, proving its effectiveness. The paper illustrates its application to the Potenza Municipality case study in which the online calculator DSTool (the core instrument of the toolbox) was utilized to select and prioritize the energy efficiency interventions in public buildings implementable in a three-year action plan in terms of costs, energy savings, CO2 emissions’ reduction and return on investments. The results highlight that improvements in the building envelopes (walls and roofs), heating and lighting and photovoltaic systems allow reducing CO2 emission approximately 644 t/year and saving about 2050 MWh/year with a total three-year investment of 1,728,823 EUR.

An assessment of CO2 emissions avoided by energy-efficiency programs: A general methodology and a case study in Brazil

Energy ◽  
2018 ◽  
Vol 142 ◽  
pp. 702-715 ◽  
Author(s):  
Nathália Duarte Braz Vieira ◽  
Luiz Augusto Horta Nogueira ◽  
Jamil Haddad
Keyword(s):  
Energy Efficiency ◽  
Co2 Emissions ◽  
General Methodology

scholarly journals Assessing the Effect of Modifying Milking Routines on Dairy Farm Economic and Environmental Performance

2021 ◽  
Vol 3 (2) ◽  
pp. 266-277
Author(s):  
Michael Breen ◽  
Michael D. Murphy ◽  
John Upton
Keyword(s):  
Co2 Emissions ◽  
Environmental Performance ◽  
Dairy Farm ◽  
Environmental Benefits ◽  
Relative Importance ◽  
Trade Offs ◽  
Net Profit ◽  
Labor Utilization ◽  
Renewable Technology

The objective of this paper was to quantify the economic and environmental effects of changing a dairy farm’s milking start times. Changing morning and evening milking start times could reduce both electricity costs and farm electricity related CO2 emissions. However, this may also involve altering farmer routines which are based on practical considerations. Hence, these changes need to be quantified both in terms of profit/emissions and in terms of how far these milking start times deviate from normal operations. The method presented in this paper optimized the combination of dairy farm infrastructure setup and morning and evening milking start times, based on a weighting variable (α) which assigned relative importance to labor utilization, farm net profit and farm electricity related CO2 emissions. Multi-objective optimization was utilized to assess trade-offs between labor utilization and net profit, as well as labor utilization and electricity related CO2 emissions. For a case study involving a 195 cow Irish dairy farm, when the relative importance of maximizing farm net profit or minimizing farm electricity related CO2 emissions was high, the least common milking start times (06:00 and 20:00) were selected. When the relative importance of labor utilization was high, the most common milking start times (07:00 and 17:00) were selected. The 195 cow farm saved €137 per annum when milking start times were changed from the most common to the least common. Reductions in electricity related CO2 emissions were also seen when the milking start times were changed from most common to least common. However, this reduction in emissions was primarily due to the addition of efficient and renewable technology to the farm. It was deduced that the monetary and environmental benefits of altering farmer milking routines were unlikely to change normal farm operating procedures.

Semantic Economic Models for Bioenergy Projects

2015 ◽  
Vol 09 (03) ◽  
pp. 333-352 ◽  
Author(s):  
Krishna Sapkota ◽  
Pathmeswaran Raju ◽  
Will Byrne ◽  
Craig Chapman
Keyword(s):  
Decision Support ◽  
Economic Model ◽  
Economic Models ◽  
Hydrocarbon Fuel ◽  
Decision Support Tools ◽  
Semantic Web Technologies ◽  
Sustainable Solutions ◽  
Web Technologies ◽  
Support Tools

One of the sustainable solutions to the depleting fossils fuels is bioenergy, which is a renewable energy generated from biomass, and biofuel is a hydrocarbon fuel that is produced from biomass. Recently, bioenergy and biofuel projects are encouraged and supported by many governments and organizations in various ways such as providing incentives, technical supports, information, and decision support tools. The economic model is one of the decision support tools, which helps to estimate the costs and earnings involved in a project. It is constructed with various elements such as concepts, relations, logics, constants, and equations. In current economic models, all the elements are hard-coded into some programming code, which makes the model less reusable and extendable. To address the issue, we present an ontology-based economic model in this paper. In particular, we have leveraged the Semantic Web technologies to represent the knowledge about the bioenergy and biofuel economics and inferred the equations and other values required for economic calculations. The case study has been carried out in two of the INTERREG Projects and found promising results.

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