dc.contributor.author |
Lunga, D
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|
dc.contributor.author |
Ward, S
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|
dc.contributor.author |
Msimango, N
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|
dc.date.accessioned |
2015-11-12T07:40:58Z |
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dc.date.available |
2015-11-12T07:40:58Z |
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dc.date.issued |
2014-11 |
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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
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|
dc.identifier.uri |
http://hdl.handle.net/10204/8273
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|
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 -
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en_ZA |