This book addresses the use of Geographic Information Systems to help evaluate cost-effectiveness of renewable resource exploitation. Offers a process to adapt geographic data for analysis and decision-making in the economic exploitation of geothermal energy.

Through the results of a developed case study of information system for low temperature geothermal energy, GIS to Support Cost-effective Decisions on Renewable Sources addresses the issue of the use of Geographic Information Systems (GIS) in evaluating cost-effectiveness of renewable resource exploitation on a regional scale. Focusing on the design of a Decision Support System, a process is presented aimed to transform geographic data into knowledge useful for analysis and decision-making on the economic exploitation of geothermal energy. This detailed description includes a literature review and technical issues related to data collection, data mining, decision analysis for the informative system developed for the case study. A multi-disciplinary approach to GIS design is presented which is also an innovative example of fusion of georeferenced data acquired from multiple sources including remote sensing, networks of sensors and socio-economic censuses. GIS to Support Cost-effective Decisions on Renewable Sources is a useful, practical reference for engineers, managers and researchers involved in the design of GIS, decision support systems, investment planning/strategy in renewable energy and ICT innovation in this field.

Presents a Geographic Information System (GIS) dedicated to the cost effectiveness analysis of a renewable energy source, the low temperature geothermal energy, which is used for heating domestic and public environments Focuses on the design of system components that transform geographic data into knowledge useful for analysis and decision-making on the economic exploitation of geothermal energy Provides a multidisciplinary approach to GIS design, which mixes the contribution from engineering, science and economics

Autorentext

Alberto Gemelli received the PhD and B.Eng. in Computer Engineering at the Polytechnic University of Marche and a MSc Geology at the Catania University. His main research interests are on applications of geographic information systems to renewable energy, economic geography and spatial data mining. In these areas he had professional assignments with several years work experience in geophysical data processing. Adriano Mancini holds a Post-doctor position at the Information Engineering Department of Polytechnic University of Marche. He received PhD and MSc in the Industrial Automation Engineering at the same university. His researched interests are on applications of geographic information systems to renewable energy, land use & land cover and image processing. He is also a consultant and scientific advisor for companies and spin-off. Claudia Diamantini is Associate Professor of Computer Engineering at the Polytechnic University of Marche, where she leads the Knowledge Discovery & Management research group. She received the Laurea degree with honors in 1990 from the University of Milan, and the PhD from the University of Ancona in 1995. Her research interests are in the areas of machine learning, data mining and knowledge discovery. Sauro Longhi is Full Professor of Automation Engineering at the Polytechnic University of Marche and Director of the Information Engineering Department. His main research interests are the model based control, fault diagnosis , unmanned systems and renewable energy. He published more than 300 papers on international journal and conferences. He was the CEO of a spin-off company focused on green energy.

Inhalt

PART I.- Preliminary Study.- 1.Decision environment of renewable energy: the case of geothermal energy.- 2.GIS supported decision making..- PART II. Case Study.- 3.GIS supported decision making for low temperature geothermal energy in Central Italy.- 4.Data collection.- 5.Feature analysis: selecting decision criteria.- 6.Decision analysis: choosing the right plant.- 7.Regional atlas supporting the decision-making process.- 8.Conclusive remarks