Which energy mix for the UK (United Kingdom)? An evolutive descriptive mapping with the integrated GAIA (graphical analysis for interactive aid)–AHP (analytic hierarchy process) visualization tool
Introduction
Energy planning is an important process that has long-range implications but unfortunately, the process is not trivial as it involves many stakeholders with different backgrounds, and has to be analysed in many contexts including the social, economic, environmental and technical contexts. To facilitate this process, MCDM (multi-criteria decision making) methods have been extensively used to prioritize available options after assessing and synthesizing all the individual preferences [1]. However, the aggregative approaches like AHP (analytic hierarchy process) have low explanatory power and results are often not enough to reach to a consensual decision by stakeholders, especially when they have divergent views [2]. It is therefore necessary to identify the points of agreements and disagreements before initiating a negotiation process. In response to this need, GAIA (graphical analysis for interactive aid) [3] was developed to capture different views of DMs (decision-makers) with respect to many criteria and to display them graphically. The GAIA method was initially proposed to complement PROMETHEE which is widely used in strategic decision making [3]. GAIA has recently been combined with Geographical Information Systems [4] and FlowSort [5].
In this paper, we propose to combine the GAIA method with AHP and show its usefulness in the area of energy policy making. We investigate the visualization of preferences and their evolution in situations where additional information is acquired during the decision making process. The proposed combination (AHP-GAIA) displays a graphical representation that can easily highlight the presence of any like-minded decision makers/agents or opposite minds, and can also reflect changes in their preferences over time. This hybrid method has been applied in a two-phase experiment. In the first phase, participants were asked to rank seven energy sources in the United Kingdom for the next 20 years. Each participant compared the options in pairs without any specific tool. For the second phase, the participants were informed about the widely-used criteria to evaluate the energy sources, and were asked to produce a documented report on how well the energy sources were fulfilling these criteria. Three months later, the participants submitted their reports that showed their analyses of the seven energy sources with the help of AHP. The adapted GAIA approach was then used to visualize the change in participants' preferences. In both phases, the solar energy has been found to be the most preferred choice, while coal remained the least preferred. Interestingly, the dispersion of the opinions decreased in the second phase i.e. more participants were found in agreement with each other after performing the detailed analysis of the selected energy sources.
The proposed technique of AHP-GAIA has been implemented in an open-source software tool – called PriEsT – that helps visualize all the preferences of multiple stakeholders in a single plot. We believe that the proposed technique can help policy makers towards understanding the preferences of each stakeholder and therefore can help towards better justification, better communication and even towards better ways for negotiation.
The remaining paper is structured as follow: Section 2 reviews the literature on energy planning with multi-criteria methods. Section 3 introduces the AHP and GAIA methods; Section 4 then proposes the use of GAIA in AHP group decision making. Section 5 presents the experimental analysis and their results; and Section 6 concludes the paper with possible future work.
Section snippets
Literature review
Energy planning is becoming more and more complex as demands are increasing for more energy while at the same time, challenges like environmental impact, safety, security, and economic viability are all pressing for a need to have better technology and tools in the field of energy production and planning. Although several methods have been proposed to assist the energy planning process, the two most widely-used techniques found in literature are the use of simulation and multi-criteria decision
Basics on AHP
AHP is a widely-used MCDM technique with the following two important features as compared to other MCDM methods [64]:
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The decision problem in AHP can be decomposed into a multi-level hierarchical structure of criteria. The criteria at the lowest level of the hierarchy are considered ‘atomic’ in a sense that they could not be decomposed further. The alternatives are then placed below these ‘atomic’ criteria.
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All the evaluations are provided through pairwise comparisons and priorities are derived
Constructing the GAIA plan
The idea of GAIA is to represent multidimensional information in a low dimensional space with as much information as possible. For example, a decision problem that involves six criteria will have six-dimensional scores assigned to each alternative, which is impossible to visualize in a conventional Euclidean space. This is sometimes referred to as “curse of dimensionality”. To solve this problem, GAIA borrows the idea of dimensionality reduction from principle component analysis – a widely used
Case study
The GAIA-AHP hybrid group decision making method and visualization has been used in a two-phase experiment to investigate the difference of intuitive versus informed and structured decision in the energy sector. Postgraduate students in the Portsmouth Business School were asked to estimate the importance of seven sources of energy production (coal, gas, nuclear, oil, solar, tidal and wind) for the next twenty years for the United Kingdom. Students are an important voting class and it is
Conclusion
Energy planning is a complex problem that has been often solved with multi-criteria decision methods. These methods have the strength to incorporate technical and subjective conflicting appreciations. In this paper, we complement AHP with a visualisation tool in an open-source software tool that helps visualize the preferences of multiple stakeholders in a single plot. This descriptive feature allows policy makers to better understand the preferences of stakeholders, and has the ability to
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