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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
De Giorgi, M., Congedo, P., Malvoni, M.: Photovoltaic power forecasting using statistical methods: impact of weather data. IET Sci. Meas. Technol. 8, 90–97 (2014)
Idrus, Z., Abdullah, N.A.S., Zainuddin, H., Ja’afar, A.D.M.: Software application for analyzing photovoltaic module panel temperature in relation to climate factors. In: International Conference on Soft Computing in Data Science, pp. 197–208 (2017)
Johansson, J., Forsell, C.: Evaluation of parallel coordinates: overview, categorization and guidelines for future research. IEEE Trans. Vis. Comput. Graph. 22, 579–588 (2016)
Idrus, Z., Bakri, M., Noordin, F., Lokman, A.M., Aliman, S.: Visual analytics of happiness index in parallel coordinate graph. In: International Conference on Kansei Engineering & Emotion Research, pp. 891–898 (2018)
Steinparz, S., Aßmair, R., Bauer, A., Feiner, J.: InfoVis—parallel coordinates. Graz University of Technolog (2010)
Heinrich, J.: Visualization techniques for parallel coordinates (2013)
Sharma, A., Sharma, M.: Power & energy optimization in solar photovoltaic and concentrated solar power systems. In: 2017 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1–6 (2017)
Lewis, N.S.: Research opportunities to advance solar energy utilization. Science 351, aad1920 (2016)
Ho, C.N.M., Andico, R., Mudiyanselage, R.G.A.: Solar photovoltaic power in Manitoba. In: 2017 IEEE Electrical Power and Energy Conference (EPEC), pp. 1–6 (2017)
Dilla, W.N., Raschke, R.L.: Data visualization for fraud detection: practice implications and a call for future research. Int. J. Account. Inf. Syst. 16, 1–22 (2015)
Schuh, M.A., Banda, J.M., Wylie, T., McInerney, P., Pillai, K.G., Angryk, R.A.: On visualization techniques for solar data mining. Astron. Comput. 10, 32–42 (2015)
Idrus, Z., Zainuddin, H., Ja’afar, A.D.M.: Visual analytics: designing flexible filtering in parallel coordinate graph. J. Fundam. Appl. Sci. 9, 23–32 (2017)
Chen, X., Jin, R.: Statistical modeling for visualization evaluation through data fusion. Appl. Ergon. 65, 551–561 (2017)
Zhou, Z., Ye, Z., Yu, J., Chen, W.: Cluster-aware arrangement of the parallel coordinate plots. J. Vis. Lang. Comput. 46, 43–52 (2017)
Palmas, G., Bachynskyi, M., Oulasvirta, A., Seidel, H.P., Weinkauf, T.: An edge-bundling layout for interactive parallel coordinates. In: 2014 IEEE Pacific Visualization Symposium (PacificVis), pp. 57–64 (2014)
Zhou, H., Xu, P., Ming, Z., Qu, H.: Parallel coordinates with data labels. In: Proceedings of the 7th International Symposium on Visual Information Communication and Interaction, p. 49 (2014)
Lima, R.S.D.A.D., Dos Santos, C.G.R., Meiguins, B.S.: A visual representation of clusters characteristics using edge bundling for parallel coordinates. In: 2017 21st International Conference Information Visualisation (IV), pp. 90–95 (2017)
Cui, W., Zhou, H., Qu, H., Wong, P.C., Li, X.: Geometry-based edge clustering for graph visualization. IEEE Trans. Vis. Comput. Graph. 14, 1277–1284 (2008)
Khalid, N.E.A., Yusoff, M., Kamaru-Zaman, E.A., Kamsani, I.I.: Multidimensional data medical dataset using interactive visualization star coordinate technique. Procedia Comput. Sci. 42, 247–254 (2014)
McDonnell, K.T., Mueller, K.: Illustrative parallel coordinates. In: Computer Graphics Forum, pp. 1031–1038 (2008)
Adhau, S.P., Moharil, R.M., Adhau, P.G.: K-means clustering technique applied to availability of micro hydro power. Sustain. Energy Technol. Assessments. 8, 191–201 (2014)
Lhuillier, A., Hurter, C., Telea, A.: State of the art in edge and trail bundling techniques. In: Computer Graphics Forum, pp. 619–645 (2017)
Lu, L.F., Huang, M.L., Zhang, J.: Two axes re-ordering methods in parallel coordinates plots. J. Vis. Lang. Comput. 33, 3–12 (2016)
Xie, W., Wei, Y., Ma, H., Du, X.: RBPCP: visualization on multi-set high-dimensional data. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 16–20 (2017)
Wang, J., Liu, X., Shen, H.-W., Lin, G.: Multi-resolution climate ensemble parameter analysis with nested parallel coordinates plots. IEEE Trans. Vis. Comput. Graph. 23, 81–90 (2017)
Beham, M., Herzner, W., Gröller, M.E., Kehrer, J.: Cupid: cluster-based exploration of geometry generators with parallel coordinates and radial trees. IEEE Trans. Vis. Comput. Graph. 20, 1693–1702 (2014)
Qingyun, L., Shu, G., Xiufeng, C., Liangchen, C.: Research of the security situation visual analysis for multidimensional inland navigation based on parallel coordinates (2015)
Raidou, R.G., Eisemann, M., Breeuwer, M., Eisemann, E., Vilanova, A.: Orientation-enhanced parallel coordinate plots. IEEE Trans. Vis. Comput. Graph. 22, 589–598 (2016)
Nguyen, H., Rosen, P.: DSPCP: a data scalable approach for identifying relationships in parallel coordinates. IEEE Trans. Vis. Comput. Graph. 24, 1301–1315 (2018)
Rosenbaum, R., Zhi, J., Hamann, B.: Progressive parallel coordinates. In: 2012 IEEE Pacific Visualization Symposium (PacificVis), pp. 25–32 (2012)
Tayfur, S., Alver, N., Abdi, S., Saatci, S., Ghiami, A.: Characterization of concrete matrix/steel fiber de-bonding in an SFRC beam: principal component analysis and k-mean algorithm for clustering AE data. Eng. Fract. Mech. 194, 73–85 (2018)
Ay, M., Kisi, O.: Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques. J. Hydrol. 511, 279–289 (2014)
Acknowledgement
The authors would like to thank Faculty of Computer and Mathematical Sciences, as well as Universiti Teknologi MARA for facilities and financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-13-3441-2_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3440-5
Online ISBN: 978-981-13-3441-2
eBook Packages: Computer ScienceComputer Science (R0)