Matthias Nielsen
Kaj Grønbæk
ABSTRACT
Domain experts dealing with big data are typically not familiar with advanced data mining tools. This especially holds true for domain experts within energy management. In this paper, we introduce a visual analytics approach that empowers such users to visually analyze energy behavior based on consumption meters, sensors and user reported survey data. The approach is aimed at visual analysis of resource consumption data and occupant survey data (e.g. from questionnaires) from apartment buildings. We discuss the principles and architecture of the affine visualization tool, AffinityViz, that interactively maps data from real world buildings. It is an overview +detail inter-active visual analytics tool supporting both rapid ad hoc explorations and structured evaluation of hypotheses about patterns and anomalies in resource consumption data mixed with occupant survey data. We have evaluated the approach with five domain experts within energy management, and further with 10 data analytics experts and found that it was easily attainable and that it supported visual analysis of mixed consumption and survey data. Finally, we discuss future perspectives of affine visual analytics for mixed, explorative visual analysis of resource consumption and occupant survey data.
- Beckel, C., L. Sadamori, T. Staake and S. Santini. "Revealing household characteristics from smart meter data." Energy 78(2014): 397--410 10.1016/j.energy.2014.10.025. Google Scholar
Cross Ref
- Becker, R. A. and W. S. Cleveland. "Brushing scatterplots." Technometrics 29(2) (1987): 127--142. Google ScholarDigital Library
- Bellala, G., M. Marwah, M. Arlitt, G. Lyon and C. Bash. Following the electrons: methods for power management in commercial buildings. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM (2012). Google ScholarDigital Library
- Bertin, J. Semiology of graphics: diagrams, networks, maps, Esri Press (2010), 978-1589482616.Google Scholar
- Blunck, H., N. O. Bouvin, J. Mose Entwistle, K. Grønbæk, M. B. Kjærgaard, M. Nielsen, M. Graves Petersen, M. K. Rasmussen and M. Wüstenberg. Computational environmental ethnography: combining collective sensing and ethnographic inquiries to advance means for reducing environmental footprints. Proceedings of the fourth international conference on Future energy systems, ACM (2013) 10.1145/2487166.2487176. Google ScholarDigital Library
- Bostock, M., V. Ogievetsky and J. Heer. "D3 data-driven documents." Visualization and Computer Graphics, IEEE Transactions on 17(12) (2011): 2301--2309 10.1109/TVCG.2011.185. Google ScholarDigital Library
- Card, S. K., J. D. Mackinlay and B. Shneiderman. Readings in information visualization: using vision to think, Morgan Kaufmann (1999), 1558605339.Google Scholar
- Chetty, M., D. Tran and R. E. Grinter. Getting to green: understanding resource consumption in the home. 10th. UbiComp Seoul, ACM(2008): 242--251 10.1145/1409635.1409668. Google ScholarDigital Library
- Chittaro, L., C. Combi and G. Trapasso. "Data mining on temporal data: a visual approach and its clinical application to hemodialysis." Journal of Visual Languages & Computing 14(6) (2003): 591--620 10.1016/j.jvlc.2003.06.003. Google ScholarCross Ref
- Christensen, H. B., H. Blunck, N. O. Bouvin, R. S. Brewer and M. Wüstenberg. Karibu: a flexible, highly-available, and scalable architecture for urban data collection. Proceedings of the First International Conference on IoT in Urban Space, ICST (2014) 10.4108/icst.urb-iot.2014.257253. Google ScholarDigital Library
- Darby, S. "The effectiveness of feedback on energy consumption." A Review for DEFRA of the Literature on Metering, Billing and direct Displays 486(2006): 2006.Google Scholar
- De Young, R. "Changing behavior and making it stick The conceptualization and management of conservation behavior." Environment and Behavior 25(3) (1993): 485--505 10.1177/0013916593253003. Google ScholarCross Ref
- Erickson, T., M. Li, Y. Kim, A. Deshpande, S. Sahu, T. Chao, P. Sukaviriya and M. Naphade. The dubuque electricity portal: evaluation of a city-scale residential electricity consumption feedback system. CHI'13. Paris, France, ACM(2013): 1203--1212 10.1145/2470654.2466155. Google ScholarDigital Library
- Froehlich, J., L. Findlater and J. Landay. The design of eco-feedback technology. CHI'10. Atlanta, Georgia, USA, ACM(2010): 1999--2008 10.1145/1753326.1753629. Google ScholarDigital Library
- Goodwin, S., J. Dykes, S. Jones, I. Dillingham, G. Dove, A. Duffy, A. Kachkaev, A. Slingsby and J. Wood. "Creative user-centered visualization design for energy analysts and modelers." Visualization and Computer Graphics, IEEE Transactions on 19(12) (2013): 2516--2525 10.1109/TVCG.2013.145. Google ScholarDigital Library
- Hanrahan, P., C. Stolte and J. Mackinlay. "Visual Analysis for Everyone." Tableau White paper 4(2007).Google Scholar
- Hunter, J. D. "Matplotlib: A 2D graphics environment." Computing in science and engineering 9(3) (2007): 90--95 10.1109/MCSE.2007.55. Google ScholarDigital Library
- Irwin, G., N. Banerjee, A. Hurst and S. Rollins. Understanding context governing energy consumption in homes. CHI '14. Toronto, Ontario, Canada, ACM(2014): 2443--2448 10.1145/2559206.2581335. Google ScholarDigital Library
- Jacobs, A., D. Kilb and G. Kent. "3-D interdisciplinary visualization: Tools for scientific analysis and communication." Seismological Research Letters 79(6) (2008): 867--876 10.1785/gssrl.79.6.867. Google ScholarCross Ref
- Kim, S. A., D. Shin, Y. Choe, T. Seibert and S. P. Walz. "Integrated energy monitoring and visualization system for Smart Green City development: Designing a spatial information integrated energy monitoring model in the context of massive data management on a web based platform." Automation in Construction 22(0) (2012): 51--59 10.1016/j.autcon.2011.07.004. Google ScholarCross Ref
- Kleiminger, W., C. Beckel, T. Staake and S. Santini. Occupancy Detection from Electricity Consumption Data. Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings. Roma, Italy, ACM(2013): 1--8 10.1145/2528282.2528295. Google ScholarDigital Library
- Knigge, L. and M. Cope. "Grounded visualization: integrating the analysis of qualitative and quantitative data through grounded theory and visualization." Environment and Planning A 38(11) (2006): 2021--2037 10.1068/a37327. Google ScholarCross Ref
- Kollmuss, A. and J. Agyeman. "Mind the gap: why do people act environmentally and what are the barriers to pro-environmental behavior?" Environmental education research 8(3) (2002): 239--260 10.1080/13504620220145401. Google ScholarCross Ref
- Kusy, B., R. Rana, P. Valencia, R. Jurdak and J. Wall. Experiences with Sensors for Energy Efficiency in Commercial Buildings. Real-World Wireless Sensor Networks. K. Langendoen, W. Hu, F. Ferrari, M. Zimmerling and L. Mottola, Springer International Publishing. 281 (2014): 231--243 10.1007/978-3-319-03071-5_23. Google ScholarCross Ref
- Martani, C., D. Lee, P. Robinson, R. Britter and C. Ratti. "ENERNET: Studying the dynamic relationship between building occupancy and energy consumption." Energy and Buildings 47(0) (2012): 584--591 10.1016/j.enbuild.2011.12.037. Google ScholarCross Ref
- Mills, E. and A. Rosenfeld. "Consumer non-energy benefits as a motivation for making energy-efficiency improvements." Energy 21(7) (1996): 707--720 10.1016/0360-5442(96)00005-9. Google ScholarCross Ref
- Nielsen, M. and K. Grønbæk. "Towards Highly Affine Visualizations of Consumption Data from Buildings." IVAPP(2015): 247--255 10.5220/0005315102470255. Google ScholarCross Ref
- Orestis, A., A. Dimitrios, D. Dimitrios and C. Ioannis. Smart energy monitoring and management in large multi-office building environments. Proceedings of the 17th Panhellenic Conference on Informatics. Thessaloniki, Greece, ACM(2013): 219--226 10.1145/2491845.2491868. Google ScholarDigital Library
- Pelletier, L. G., K. M. Tuson, I. Green Demers, K. Noels and A. M. Beaton. "Why are you doing things for the environment? The motivation toward the environment scale (mtes) 1." Journal of Applied Social Psychology 28(5) (1998): 437--468 10.1111/j.1559-1816.1998.tb01714.x. Google ScholarCross Ref
- Pierce, J., W. Odom and E. Blevis. Energy aware dwelling: a critical survey of interaction design for eco-visualizations. 20th. OZCHI. Cairns, Australia, ACM(2008): 1--8 10.1145/1517744.1517746. Google ScholarDigital Library
- Pierce, J. and E. Paulos. Beyond energy monitors: interaction, energy, and emerging energy systems. CHI'12. Austin, Texas, USA, ACM(2012): 665--674 10.1145/2207676.2207771. Google ScholarDigital Library
- Reeves, B., J. J. Cummings, J. K. Scarborough, J. Flora and D. Anderson. Leveraging the engagement of games to change energy behavior. Collaboration Technologies and Systems (CTS), 2012 International Conference on(2012) 10.1109/CTS.2012.6261074. Google ScholarCross Ref
- Rodgers, J. and L. Bartram. "Exploring Ambient and Artistic Visualization for Residential Energy Use Feedback." Visualization and Computer Graphics, IEEE Transactions on 17(12) (2011): 2489--2497 10.1109/TVCG.2011.196. Google ScholarDigital Library
- Shneiderman, B. The eyes have it: A task by data type taxonomy for information visualizations, IEEE (1996), 081867508X.Google Scholar
- Strengers, Y. A. A. Designing eco-feedback systems for everyday life. CHI'11. Vancouver, BC, Canada, ACM(2011): 2135--2144 10.1145/1978942.1979252. Google ScholarDigital Library
- Ware, C. Visual Thinking: for Design, Morgan Kaufmann Publishers Inc. (2008), 0123708966, 9780123708960.Google Scholar
- Yang, X. and S. Ergan. "Evaluation of visualization techniques for use by facility operators during monitoring tasks." Automation in Construction 44(0) (2014): 103--118 10.1016/j.autcon.2014.03.023. Google ScholarCross Ref
Index Terms
- Supporting interactive visual analytics of energy behavior in buildings through affine visualizations
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