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Interactive energy consumption visualization

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dc.contributor.author Lunga, D
dc.contributor.author Ward, S
dc.contributor.author Msimango, N
dc.date.accessioned 2015-11-12T07:40:58Z
dc.date.available 2015-11-12T07:40:58Z
dc.date.issued 2014-11
dc.identifier.citation Lunga, D, Ward, S and Msimango, N. 2014. Interactive energy consumption visualization. In: RobMech/PRASA/AfLaT symposium 2014, Logoon Hotel, Cape Town, 27-28 November 2014 en_US
dc.identifier.isbn 978-0-620-62617-0
dc.identifier.uri http://www.prasa.org/proceedings/2014/prasa2014-42.pdf
dc.identifier.uri http://hdl.handle.net/10204/8273
dc.description RobMech/PRASA/AfLaT symposium 2014, Logoon Hotel, Cape Town, 27-28 November 2014 en_US
dc.description.abstract Interactive data visualization is a transformational technique that can turn raw data into immersive insights extraction. In this study, an interactive dashboard that visualizes raw aggregated data is developed to identify energy usage patterns in an office building environment. The main goal is to highlight high consumptions patterns, estimate costs and savings, and recommend energy saving strategies. In its useful nature, the dashboard can provide valuable information for further programs tied to energy management systems. The dashboard’s interface caters for different users that include a building operational manager, and the building occupants. In its demonstration, the visualization interface will show how to incorporate data filtering algorithms or multiple facets to enable multidimensional scales of viewing the same data (i.e. day, week, month, and year). The dashboard successfully highlights high energy usage intervals with bar, line, and heat map charts. In addition, the interface incorporates interactivity to ensure that the display allows for the user to submit instant uncanned queries. For future work, algorithmic approaches that include machine learning will aim to incorporate demographic data to establish human behavioural patterns and activities related to energy usage. en_US
dc.language.iso en en_US
dc.publisher Pattern Recognition Association of South Africa en_US
dc.relation.ispartofseries Workflow;13841
dc.subject Interactive data visualization en_US
dc.subject Immersive insights extraction en_US
dc.subject Interactive energy consumption visualization en_US
dc.subject Multidimensional scales en_US
dc.subject Algorithmic approaches en_US
dc.subject Energy management systems en_US
dc.title Interactive energy consumption visualization en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Lunga, D., Ward, S., & Msimango, N. (2014). Interactive energy consumption visualization. Pattern Recognition Association of South Africa. http://hdl.handle.net/10204/8273 en_ZA
dc.identifier.chicagocitation Lunga, D, S Ward, and N Msimango. "Interactive energy consumption visualization." (2014): http://hdl.handle.net/10204/8273 en_ZA
dc.identifier.vancouvercitation Lunga D, Ward S, Msimango N, Interactive energy consumption visualization; Pattern Recognition Association of South Africa; 2014. http://hdl.handle.net/10204/8273 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Lunga, D AU - Ward, S AU - Msimango, N AB - Interactive data visualization is a transformational technique that can turn raw data into immersive insights extraction. In this study, an interactive dashboard that visualizes raw aggregated data is developed to identify energy usage patterns in an office building environment. The main goal is to highlight high consumptions patterns, estimate costs and savings, and recommend energy saving strategies. In its useful nature, the dashboard can provide valuable information for further programs tied to energy management systems. The dashboard’s interface caters for different users that include a building operational manager, and the building occupants. In its demonstration, the visualization interface will show how to incorporate data filtering algorithms or multiple facets to enable multidimensional scales of viewing the same data (i.e. day, week, month, and year). The dashboard successfully highlights high energy usage intervals with bar, line, and heat map charts. In addition, the interface incorporates interactivity to ensure that the display allows for the user to submit instant uncanned queries. For future work, algorithmic approaches that include machine learning will aim to incorporate demographic data to establish human behavioural patterns and activities related to energy usage. DA - 2014-11 DB - ResearchSpace DP - CSIR KW - Interactive data visualization KW - Immersive insights extraction KW - Interactive energy consumption visualization KW - Multidimensional scales KW - Algorithmic approaches KW - Energy management systems LK - https://researchspace.csir.co.za PY - 2014 SM - 978-0-620-62617-0 T1 - Interactive energy consumption visualization TI - Interactive energy consumption visualization UR - http://hdl.handle.net/10204/8273 ER - en_ZA


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