MULTIPLE CRITERIA DECISION SUPPORT SYSTEM FOR THE ASSESSMENT OF ENERGY GENERATION TECHNOLOGIES CONSIDERING THE DIMENSION OF VALUES

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

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.

FACILITATING THE HOUSING BARGAINING WITH THE HELP OF THE BARGAINING DECISION SUPPORT SYSTEM

More than 90 percent home buyers today rely on the Internet as one of their primary research sources and real estate related searches continually grows. Internet helps buyers to find and select bigger number of right homes for sale in a shorter time, so provides more alternatives for bargaining. The bargaining is an inseparable part of the home buying and selling process. However, housing bargaining mostly is conducted face-to face, so there is a growing need for facilitating the housing bargaining and conducting such bargaining on the Web with the help of the systems. The article describes the developed Real-Time Housing Multiple Criteria Bargaining Decision Support System, based on multiple-criteria mathematical methods, which helps to improve the efficiency of bargaining through the following functions: search for housing alternatives; formulation of the initial comparative table of alternatives; multiple criteria analysis of housing alternatives and negotiation tactics; determination of the most useful home option for buying; presentation of recommendations and real-time determination of a home's market value; e-bargaining using templates of bargaining e-mails generated by the system.