Decision Support System for Sustainability Assessment of Power Generation Technologies - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 526

Quite a few decision-making methods, expert decision support systems, neural networks,
spreadsheets and analysers to facilitate decision-making are currently available worldwide.
Decision support systems, by the type of support, are classified as follows: 1) individual
decision support systems (IDSS); 2) group decision support systems (GDSS); 3) negotiation
support systems (NSS), and 4) expert systems (ES). Expert systems are intended as a tool for
skilled professionals of certain fields, for experts. Therefore, expert systems must
incorporate a range of indicators that summarise the specific field-related knowledge and
comprehensive information that describes the issue in question. The system “recognises” the
situation, identifies the diagnosis, gives questions, and recommends decisions. These
systems have many secondary functions: they make questions, model alternative decisions,
and offer conclusions and recommendations.
Expert systems are gradually becoming an inseparable part of any decision-making process.
Expert decision support systems are widely used in environmental studies, in political
decision-making, in evaluations of strategies, development plans and projects, and in
selection of alternatives, when such selection is not easy due to abundant factors that may
affect the decision.
Expert systems may be classified into two groups:
1. Working expert systems: they are elaborated and approved expert systems continually
used in decision-making and undergoing continual improvement (Svensson, 2002;
Masood & Soo, 2002).
2. Conceptual expert systems: any systems in development and any expert systems for
initial scientific assessment (Benavides & Prado, 2002).
Decision support systems successfully helped to handle a multiple criteria problem in the
energy sector. One decision support system was developed specifically for Ghana and used
in environmental research as a tool to justify the decisions related to restructuring of the
electrical power industry (Bergey et al., 2003). One application was developed as a tool to
analyse the dynamic price changes in the US wholesale market of electric power. This
software is used for practical applications and is an effective instrument in modelling and
simulation of situations in the market of electrical power trade (Sueyoshi & Tadiparthi,
2008). Literature sources cite a multiple criteria problem that was handled selecting
competitive nuclear technologies, assessing environmental factors, and performing the
analysis of alternatives. This decision-making process was based on the AHP methodology,
which was also the basis for the relevant decision support system (Deok & Johoo, 2010; Shen
et al., 2010). Decision support systems are used to select and maintain renewable energy
technologies (Yue & Grant, 2007; Sliogeriene et al., 2009). They are also used to analyse the
energy policies of EU member states, to set uniform evaluation criteria that help to consider
countries with different levels of development and to assess their energy development in
line with the principles of sustainable development, to assess the expansion of renewable
energy sources, environment protection, and the performance of the energy sector
(Patlitzianas & Psarras, 2007; Drozd, 2003). The ELECTRE methodology was used to
develop a decision support system, which analyses the instruments that can help to improve
the efficiency of energy consumption in view of environmental factors. Decision models also
incorporate the cost-benefit analysis thus enabling the decision-maker to verify the final
outcome of each decision (Neves et al., 2008). More decision support systems based on a
number of multiple criteria analysis methodologies are available. But all these systems share
one advantage: they can process huge amounts of information, and assess how the
dynamically fluctuating impact of the environment affects the decisions.