Abstract
The Danish path to a sustainable energy system focuses on increasing energy efficiency and flexible consumption via smart grid technologies. Information and communication technology is fundamental for achieving these goals by enabling among others new methods and systems for data collection and decision support. This book chapter covers new data collection options exemplified in the concrete case of a living lab for smart grid technologies. Furthermore, the chapter covers the use of visualisation to design decision support for such collected data. We formulate energy management based on energy data as a visualisation problem in the nested model for information visualisation. We prototype a visualisation tool chain to produce a rich set of visualisations based on energy data from five commercial and industrial buildings. Finally, we present qualitative study results for the value of visualisations as an analytical tool. Building on the results we identify important information needs for users of data analysis tools.
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References
Amar, R.A., Eagan, J., Stasko, J.T.: Low-level components of analytic activity in information visualization. In: InfoVis, IEEE Computer Society, p. 15 (2005)
Braga, L., Braga, A., Braga, C.: On the characterization and monitoring of building energy demand using statistical process control methodologies. Energy Build. 65, 205–219 (2013)
Brandstatt, C., Friedrichsen, N., Meyer, R., Palovic, M.: Roles and responsibilities in smart grids: a country comparison. In: Proceedings of the 9th International Conference on European Energy Market (EEM) (2012)
Costanza, E., Ramchurn, S.D., Jennings, N.R.: Understanding domestic energy consumption through interactive visualisation: a field study. In: UbiComp, ACM, pp. 216–225 (2012)
DEA: Energy consumption of buildings—http://www.ens.dk/byggeri/byggeriets-energiforbrug (2015)
Eccleston, C., March, F., Cohen, T.: Inside Energy: Developing and Managing an ISO 50001 Energy Management System. CRC Press, Boca Raton, FL (2011)
ENE: http://www.greentechcenter.dk/uk/ (2015)
Froehlich, J., Findlater, L., Landay, J.A.: The design of eco-feedback technology. In: CHI, ACM, pp. 1999–2008 (2010)
Goodwin, S., Dykes, J., Jones, S., Dillingham, I., Dove, G., Duffy, A., Kachkaev, A., Slingsby, A., Wood, J.: Creative user-centered visualization design for energy analysts and modelers. IEEE Trans. Vis. Comput. Graph. 19(12), 2516–2525 (2013)
Granderson, J., Piette, M., Ghatikar, G., Price, P. Building energy information systems: state of the technology and user case studies (2009)
Granderson, J., Piette, M., Ghatikar, G.: Building energy information systems: user case studies. Energ. Effi. 4(1), 17–30 (2011)
Hasenfratz, D., Saukh, O., Walser, C., Hueglin, C., Fierz, M., Thiele, L.: Pushing the spatio-temporal resolution limit of urban air pollution maps. In: IEEE PerCom, pp. 69–77 (2014)
Jung, D., Krishna, V.B., Khiem, N.Q.M., Nguyen, H.H., Yau, D.K.Y.: Energytrack: Sensor-driven energy use analysis system. In: BuildSys, ACM, pp. 6:1–6:8 (2013)
KEB: Smart grid strategy—the intelligent energy system of the future (2013)
Munzner, T.: A nested process model for visualization design and validation. IEEE Trans. Vis. Comput. Graph. 15(6), 921–928 (2009)
Rollins, S., Banerjee, N.: Using rule mining to understand appliance energy consumption patterns. In: IEEE Percom, pp. 29–37 (2014)
Ruiz, A.J.R., Blunck, H., Prentow, T.S., Stisen, A., Kjærgaard, M.B.: Analysis methods for extracting knowledge from large-scale wifi monitoring to inform building facility planning. In: IEEE PerCom, pp. 130–138 (2014)
Sedlmair, M., Meyer, M.D., Munzner, T.: Design study methodology: reflections from the trenches and the stacks. IEEE Trans. Vis. Comput. Graph. 18(12), 2431–2440 (2012)
Seem, J.E.: Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy Build. 39(1), 52–58 (2007)
Sun: Sunsetlib—java. https://github.com/mikereedell/sunrisesunsetlib-java (2015)
Weiss, M., Helfenstein, A., Mattern, F., Staake, T.: Leveraging smart meter data to recognize home appliances. In: IEEE Pervasive, pp. 190–197 (2012)
Yang, L., Ting, K., Srivastava, M.B.: Inferring occupancy from opportunistically available sensor data. In: IEEE PerCom (2014)
Zhao, X., Gordon, M., Lind, M., Østergaard, J.: Towards a danish power system with 50 (2009)
Acknowledgments
The authors would like to thank European Regional Development Fund (The Region of Southern Denmark) and Regional Commercial Development Fond for funding the Micro Grid Living Lab project.
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Kjærgaard, M.B., Ma, Z., Holmegaard, E., Jørgensen, B.N. (2016). Energy Efficiency in a Mobile World. In: Beaulieu, A., de Wilde, J., Scherpen, J. (eds) Smart Grids from a Global Perspective. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-28077-6_16
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DOI: https://doi.org/10.1007/978-3-319-28077-6_16
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