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Forecasting and Visualization of Renewable Energy Technologies Using Keyword Taxonomies

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8817)

Abstract

Interest in renewable energy has grown rapidly, driven by widely held concerns about energy sustainability and security. At present, no single mode of renewable energy generation dominates and consideration tends to center on finding optimal combinations of different energy sources and generation technologies. In this context, it is very important that decision makers, investors and other stakeholders are able to keep up to date with the latest developments, comparative advantages and future prospects of the relevant technologies. This paper discusses the application of bibliometrics techniques for forecasting and integrating renewable energy technologies. Bibliometrics is the analysis of textual data, in this case scientific publications, using the statistics and trends in the text rather than the actual content. The proposed framework focuses on a number of important capabilities. Firstly, we are particularly interested in the detection of technologies that are in the early growth phase, characterized by rapid increases in the number of relevant publications. Secondly, there is a strong emphasis on visualization rather than just the generation of ranked lists of the various technologies. This is done via the use of automatically generated keyword taxonomies, which increase reliability by allowing the growth potentials of subordinate technologies to be aggregated into the overall potential of larger categories. Finally, by combining the keyword taxonomies with a colour-coding scheme, we obtain a very useful method for visualizing the technology “landscape”, allowing for rapidly evolving branches of technology to be easily detected and studied.

Keywords

  • Renewable Energy
  • Term Frequency
  • Renewable Energy Technology
  • Growth Indicator
  • Term Extraction

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    http://www.scopus.com

  2. 2.

    http://code.google.com/p/pydot/

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Woon, W.L., Aung, Z., Madnick, S. (2014). Forecasting and Visualization of Renewable Energy Technologies Using Keyword Taxonomies. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2014. Lecture Notes in Computer Science(), vol 8817. Springer, Cham. https://doi.org/10.1007/978-3-319-13290-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-13290-7_10

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