Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment

Arturas Kaklauskas,Gintautas Dzemyda,Jurga Naimaviciene,Ihar Voitau,Loreta Kanapeckiene,Olga Kurasova,Yauheni Rassokha,Laura Tupenaite

doi:10.3390/en11081994

Abstract

Implementing energy-efficient solutions in a built environment is important for reaching international energy reduction targets. For advanced energy efficiency-related solutions, computer-based decision support systems are proposed and rapidly used in a variety of spheres relevant to a built environment. Present research proposes a novel artificial neural network-based decision support system for development of an energy-efficient built environment. The system was developed by integrating methods of the multiple criteria evaluation and multivariant design, determination of project utility and market value, and visual data mining by artificial neural networks. It enables a user to compose up to 100,000,000 combinations of the energy-efficient solutions, analyze strengths and weaknesses of a built environment projects, provide advice for stakeholders, and calculate market value and utility degree of the projects. For visual data mining, self-organizing maps (type neural networks) are used, which may influence the choosing of the final set of alternatives and criteria in the decision-making problem, taking into account the discovered similarities of alternatives or criteria. A system was validated by the real case study on the design of an energy-efficient individual house.

Highlights

At the global level, buildings and the construction industry together consume about 36% of energy and generate 39% of related carbon dioxide (CO2 ) emissions [1]
The effectiveness of energy-efficient built environment development solutions shall be considered throughout the entire life cycle, from the conceptual and design stages to construction, operation, maintenance, demolition, and utilization
The values of the qualitative criteria are usually subjective; use of the expert methods that contribute to objectivity for their assessment is recommended

Summary

Introduction

Buildings and the construction industry together consume about 36% of energy and generate 39% of related carbon dioxide (CO2 ) emissions (taking into account electricity production) [1]. The European Union (EU) aims to achieve 20% energy savings by 2020 (compared with the projected energy use in 2020). Its target is to reduce primary energy consumption at least to 1483 million tons of an oil equivalent (Mtoe) and a final energy consumption at least to 1086 Mtoe in. In 2016, primary energy consumption in the EU was 4% off the efficiency target [2]. To reduce final energy consumption and to achieve the intended targets, stakeholders (governmental institutions, construction and real estate business enterprises, households, individuals) have to modify a built environment by substantial means and search for new energy-efficiency improvement solutions.

Methods

Results

Discussion

Conclusion