Difference between revisions of "1718t1is428T15"

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Experts have warned that power demand is set to double by 2030 globally despite authoritative control. High power consumption can already be observed locally. According Energy Market Authority (EMA), Singapore has faced increasing power consumption from 1965 to 2013 <ref>https://www.ema.gov.sg/Publications_Annual_Reports.aspx</ref>.  
 
Experts have warned that power demand is set to double by 2030 globally despite authoritative control. High power consumption can already be observed locally. According Energy Market Authority (EMA), Singapore has faced increasing power consumption from 1965 to 2013 <ref>https://www.ema.gov.sg/Publications_Annual_Reports.aspx</ref>.  
[[File:IS415-Group2-OnTheFly-EMA.png|700px|center]]
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[[File:1718t1is428T15-Motivation.png|700px|center]]
As Singapore is land-scarce and does not have significant renewable energy options such as hydro-power, wave, or sufficient land for mass solar energy production, energy has been a top concern in the urban nation<ref>http://www.eco-business.com/news/tackling-energy-challenges-the-singapore-way/</ref>. It is thus important to promote energy saving concepts to the public as well as deploying energy saving solution island wide. However, the usual analysis tools are not enough to provide a different perspective to facilitate the deployment of the solution. Information about the energy consumption levels of residents in Singapore are often not conveyed adequately enough in geographical terms. While EMA and Singstat provide annual data and reports on energy usage in Singapore, they lack the element of geographical positioning of the data points.  
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As Singapore is land-scarce and does not have significant renewable energy options such as hydro-power, wave, or sufficient land for mass solar energy production, energy has been a top concern in the urban nation<ref>http://www.eco-business.com/news/tackling-energy-challenges-the-singapore-way/</ref>. It is thus important to promote energy saving concepts to the public as well as deploying energy saving solution island wide. However, the usual analysis tools are not enough to provide a different perspective to facilitate the deployment of the solution. Information about the energy consumption levels of residents in Singapore are often not conveyed adequately enough in data visualisation. While EMA and Singstat provide annual data and reports on energy usage in Singapore, a powerful visualisation technique should be used to gain insights effectively.
 
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==<div style="margin-top: 10px;font-family: Helvetica; text-align: left;font-size:20px; border: 5px solid #00000000; border-radius:5px; text-align:center; background-color: #708090; color: white; padding: 2px"><span style="font-size:24px;">P</span>roject <span style="font-size:24px">O</span>bjective</div>==
 
==<div style="margin-top: 10px;font-family: Helvetica; text-align: left;font-size:20px; border: 5px solid #00000000; border-radius:5px; text-align:center; background-color: #708090; color: white; padding: 2px"><span style="font-size:24px;">P</span>roject <span style="font-size:24px">O</span>bjective</div>==
Our team aims to create a web application (Enerlyst) using R that leverages on energy datasets provided by EMA to perform geospatial analysis to identify energy usage clusters. Further analysis can then be performed to identify root causes for high or low energy consumption in these clusters and devise ways to achieve energy conversation as a nation. Project Enerlyst aims to provide a spatial perspective by utilising the following approaches: <br /><br />
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Our team aims to create a visualisation that leverages on energy datasets provided by EMA to perform spatial analysis to identify energy usage clusters with hexagonal binning. .  
  
*Choropleth Map
 
 
*Local Moran's I
 
 
*Local Indicators of Spatial Association (LISA)
 
 
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==<div style="margin-top: 10px;font-family: Helvetica; text-align: left;font-size:20px; border: 5px solid #00000000; border-radius:5px; text-align:center; background-color: #708090; color: white; padding: 2px"><span style="font-size:24px;">D</span>ataset</div>==
 
==<div style="margin-top: 10px;font-family: Helvetica; text-align: left;font-size:20px; border: 5px solid #00000000; border-radius:5px; text-align:center; background-color: #708090; color: white; padding: 2px"><span style="font-size:24px;">D</span>ataset</div>==
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The analysis will be based on EMA dataset:
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* Public housing's average monthly household electricity consumption (kwh) (2013 - 2015)
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* Private apartment's average monthly household electricity consumption (kwh) (2013 - 2015)
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Revision as of 20:47, 12 October 2017

OnTheFlyLogo.png


PROJECT PROPOSAL

PROJECT POSTER

PROJECT APPLICATION

RESEARCH PAPER


Project Motivation

Experts have warned that power demand is set to double by 2030 globally despite authoritative control. High power consumption can already be observed locally. According Energy Market Authority (EMA), Singapore has faced increasing power consumption from 1965 to 2013 [1].

1718t1is428T15-Motivation.png

As Singapore is land-scarce and does not have significant renewable energy options such as hydro-power, wave, or sufficient land for mass solar energy production, energy has been a top concern in the urban nation[2]. It is thus important to promote energy saving concepts to the public as well as deploying energy saving solution island wide. However, the usual analysis tools are not enough to provide a different perspective to facilitate the deployment of the solution. Information about the energy consumption levels of residents in Singapore are often not conveyed adequately enough in data visualisation. While EMA and Singstat provide annual data and reports on energy usage in Singapore, a powerful visualisation technique should be used to gain insights effectively.


Project Objective

Our team aims to create a visualisation that leverages on energy datasets provided by EMA to perform spatial analysis to identify energy usage clusters with hexagonal binning. .


Dataset

The analysis will be based on EMA dataset:

  • Public housing's average monthly household electricity consumption (kwh) (2013 - 2015)
  • Private apartment's average monthly household electricity consumption (kwh) (2013 - 2015)



Related Works

Inspirations

EMA publishes energy statistics on an annual basis to provide readers with a comprehensive understanding of the Singapore energy landscape through a detailed coverage of various energy-related topics. As project Enerlyst focuses on analysing households' energy consumption, only private and public households data will be used. This study will be based on EMA dataset from 2013 to 2015. 2013 data will be prepared manually whereas 2014 and 2015 data will be uploaded to the application and process on the fly.


Proposed Storyboard

Technical Challenges

Timeline

Week No(s). Task Status
3 Form team Completed
4-5 Discuss and choose a project topic Completed
6-9 Research chosen project topic and data collection Completed
10-11 Create project repository and web application planning Completed
12 Create project wiki Completed
11-15 Develop application Completed
14-15 Research report Completed
16 Finalize application Completed
16 Finalize Poster and Research Paper Completed
16 Prepare for Townhall Presentation Completed
16 Townhall Poster Presentation Completed
16 Final Project Submission Completed


Technologies/Tools

Reference


Comments