Data Mining in Decision Support for Bioenergy Production

Data Mining in Decision Support for Bioenergy Production

Nasser Ayoub (Tokyo Institute of Technology, Japan and Helwan University, Egypt) and Yuji Naka (Tokyo Institute of Technology, Japan)
DOI: 10.4018/978-1-60566-230-5.ch014
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Abstract

This chapter presents Data Mining, DM, as a planning and decision support tool for biomass resources management to produce bioenergy. Furthermore, the decision making problem for bioenergy production is defined. A Decision Support System, DSS that utilizes a DM technique, e.g. clustering, integrated with other group of techniques and tools, such as Genetic Algorithms, GA, Life Cycle Assessment, Geographical Information System, GIS, etc, is presented. A case study that shows how to tackle the decision making problem is also shown.
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Decision Making Problems

Planning and decision-making processes are in most, if not all, cases take a multidisciplinary course of actions; as the decisions have to address many involved stakeholders. Consequently, decisions are seen from multiple perspectives that mostly have tradeoff nature. In addition, the methods of applying the decisions are of prime importance to the overall project efficiency (Basson & Petrie, 2007; Hoskinson, Rope, & Fink, 2007; Marquez & Blanchar, 2006). This means that the success of the decisions made lies, primarily, on their follow up and evaluation rather than their appropriateness only. The decisions assessments, always, provide an aid in judging their suitability for specific actions and, mostly, help in stimulating better results by changing or modifying their drawbacks at early stages of bringing them into reality. The data being analyzed are often historical in nature: daily, weekly and yearly results (Chau, Cao, Anson, & Zhang, 2003) that is a fertile land for DM to be applied.

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