Assessing energy-saving measures in buildings through an intelligent decision support model

Doukas, Haris; Nychtis, Christos; Psarras, John

doi:10.1016/j.buildenv.2008.03.006

Cited by 120 publications

(58 citation statements)

References 11 publications

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“…These problems may be tackled using multi-objective optimization models, in which the set of potential alternatives is implicitly defined by constraints defining a feasible region and multiple objective functions are optimized, or multi-criteria decision analysis, in which the alternatives are explicitly known apriori to be appraised by (qualitative and/or quantitative) multiple criteria. Simulation techniques are also used to deal with this problem, in general focusing on particular aspects rather than following a global approach (Asadi, Silva, Antunes, Dias, & Glicksman, 2014;Caccavelli & Gugerli, 2002;Chidiac, Catania, & Morofsky, 2011a;Chidiac, Catania, Morofsky, & Foo, 2011b;Diakaki, Grigoroudis, & Kolokotsa, 2008;Doukas, Nychtis, & Psarras, 2009;Soares et al, in press;Verbruggen, Al Marchohi, & Janssens, 2011).…”

Section: Risk Uncertainty and Real Options In Energy Retrofit Of Buimentioning

confidence: 99%

A comparison between cost optimality and return on investment for energy retrofit in buildings-A real options perspective

Tadeu

Alexandre

Tadeu

et al. 2016

Sustainable Cities and Society

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a b s t r a c tEuropean Union (EU) regulations aim to ensure that the energy performance of buildings meets the cost-optimality criteria for energy efficiency measures. The methodological framework proposed in EU Delegated Regulation 244 is addressed to national authorities (not investors); the optimal cost level is calculated to develop regulations applicable at domestic level. Despite the complexity and the large number of possible combinations of economically viable efficiency measures, the real options for improving energy performance available to decision makers in building retrofit can be established. Our study considers a multi-objective optimization approach to identify the minimum global cost and primary energy needs of 154,000 combinations of energy efficiency measures. The proposed model is solved by the NSGA-II multi-objective evolutionary algorithm. As a result, the cost-optimal levels and a return on investment approach are compared for a set of suitable solutions for a reference building. Eighteen combinations of retrofit measures are selected and an analysis of the influence of real options on investments is proposed. We show that a sound methodological approach to determining the advantages of this type of investment should be offered so that Member States can provide valuable information and ensure that the minimum requirements are profitable to most investors.

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Section: Risk Uncertainty and Real Options In Energy Retrofit Of Buimentioning

confidence: 99%

A comparison between cost optimality and return on investment for energy retrofit in buildings-A real options perspective

Tadeu

Alexandre

Tadeu

et al. 2016

Sustainable Cities and Society

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“…Energijos taupymo priemonių įvertinimas priklauso nuo pasirinktų kriterijų. Dažniausiai taikomas ekonominis kriterijus (Doukas et al 2009;Petersen, Svendsen 2012). Dažnai energijos taupymo priemonės nėra ekonomiškai patrauklios arba, formuojant renovacijos paketus remiantis tik ekonominiu vertinimu ir siekiant kuo mažesnių energijos sąnaudų, renovacija gali gerokai pabloginti vidaus aplinkos kokybę (Brown et al 2013).…”

Section: įVadasunclassified

Analysis of the Monitoring Results of Modernized Residential Multi-storey Buildings

Rogoža

Šiupšinskas

Valančius

et al. 2017

Science - Future of Lithuania

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“…Energy management is considered as a significant factor to the energy saving of an operational unit of production. The basic concept of energy management is the continuous, systematic and well-organized audit of energy consumption, aiming at energy cost optimization with respect to energy demands, user characteristics, funding opportunities, financing ability and emission reductions achieved [9]. Moreover, ten modules are presented in order to deal with the health issues for buildings.…”

Section: Intelligent Buildingsmentioning

confidence: 99%

Intelligent Building Management Systems by Using Hardware Multi Agents: Fuzzy Approach

Naji¹,

Meybodi²,

Moghaddam³

2011

IJCA

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Growing needs of humanity have forced him to use modern technology in all aspects of his life. Intelligent buildings are one of these modern technologies that have great advantages for people. These buildings include a set of integrated and dynamic systems to manage the allocation of resources and facilitate the residents' lives. In this context and due to high speed, reliability and better respond to environmental changes, we simulate such environment using multi agents with fuzzy approach. To do this, we use Matlab and its fuzzy toolbox. The results shows that parallel processing nature in this kind of multi agents improve the speedup of the system.

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Assessing energy-saving measures in buildings through an intelligent decision support model

Cited by 120 publications

References 11 publications

A comparison between cost optimality and return on investment for energy retrofit in buildings-A real options perspective

A comparison between cost optimality and return on investment for energy retrofit in buildings-A real options perspective

Analysis of the Monitoring Results of Modernized Residential Multi-storey Buildings

Intelligent Building Management Systems by Using Hardware Multi Agents: Fuzzy Approach

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