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Clutter-Reduction Technique of Parallel Coordinates Plot for Photovoltaic Solar Data

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Soft Computing in Data Science (SCDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 937))

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Abstract

Solar energy supplies pure environmental-friendly and limitless energy resource for human. Although the cost of solar panels has declined rapidly, technology gaps still exist for achieving cost-effective scalable deployment combined with storage technologies to provide reliable, dispatchable energy. However, it is difficult to analyze a solar data, in which data was added in every 10 min by the sensors in a short time. These data can be analyzed easier and faster with the help of data visualization. One of the popular data visualization methods for displaying massive quantity of data is parallel coordinates plot (PCP). The problem when using this method is this abundance of data can cause the polylines to overlap on each other and clutter the visualization. Thus, it is difficult to comprehend the relationship that exists between the parameters of solar data such as power rate produced by solar panel, duration of daylight in a day, and surrounding temperature. Furthermore, the density of overlapped data also cannot be determined. The solution is to implement clutter-reduction technique to parallel coordinate plot. Even though there are various clutter-reduction techniques available for visualization, they are not suitable for every situation of visualization. Thus this research studies a wide range of clutter-reduction techniques that has been implemented in visualization, identifies the common features available in clutter-reduction technique, produces a conceptual framework of clutter-reduction technique as well as proposes the suitable features to be added in parallel coordinates plot of solar energy data to reduce visual clutter.

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Acknowledgement

The authors would like to thank Faculty of Computer and Mathematical Sciences, as well as Universiti Teknologi MARA for facilities and financial support.

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Authors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Muhaafidz Md Saufi, Zainura Idrus & Nur Atiqah Sia Abdullah

  2. Advanced Analytic Engineering Center (AAEC), Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Sharifah Aliman

Authors
  1. Muhaafidz Md Saufi
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  2. Zainura Idrus
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  3. Sharifah Aliman
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  4. Nur Atiqah Sia Abdullah
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Corresponding author

Correspondence to Muhaafidz Md Saufi .

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Editors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Bee Wah Yap

  2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Azlinah Hj Mohamed

  3. Department of Electrical Engineering and Computer Science, University of Tennessee at Knoxville, Knoxville, TN, USA

    Michael W. Berry

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Saufi, M.M., Idrus, Z., Aliman, S., Abdullah, N.A.S. (2019). Clutter-Reduction Technique of Parallel Coordinates Plot for Photovoltaic Solar Data. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_26

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  • DOI: https://doi.org/10.1007/978-981-13-3441-2_26

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  • Print ISBN: 978-981-13-3440-5

  • Online ISBN: 978-981-13-3441-2

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