Difference between revisions of "Charge Metrics Proposal"
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[[File:Photo_2018-10-12_20-16-07.png|250px|center|IS428_ChargeMetrics_Project]] | [[File:Photo_2018-10-12_20-16-07.png|250px|center|IS428_ChargeMetrics_Project]] | ||
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<font color="#000000" size=2><b>PROPOSAL</b></font>]] | <font color="#000000" size=2><b>PROPOSAL</b></font>]] | ||
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==<div style="margin-top: 6px;font-weight:bold;text-align: left;font-size:20px; border-bottom:5px solid #ffcc33;text-align:center; background-color: #000000; color: #ffcc33; padding: 2px; font-family:sans-serif;">Prototype</div>== | ==<div style="margin-top: 6px;font-weight:bold;text-align: left;font-size:20px; border-bottom:5px solid #ffcc33;text-align:center; background-color: #000000; color: #ffcc33; padding: 2px; font-family:sans-serif;">Prototype</div>== | ||
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===Landing Page=== | ===Landing Page=== | ||
<center> [[File: Chargemetrics Prototype.jpg|800px|Prototype 1]] </center> <br /> | <center> [[File: Chargemetrics Prototype.jpg|800px|Prototype 1]] </center> <br /> | ||
[1]Logo <br /> | [1]Logo <br /> | ||
− | [2]Bivariate | + | [2]Bivariate Choropleth Map<br /> |
[3]Filter <br /> | [3]Filter <br /> | ||
[4]Button to Historical Trend Page<br /> | [4]Button to Historical Trend Page<br /> | ||
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Project Schedule on Google Sheet:https://docs.google.com/spreadsheets/d/1IlT3Na8Ujlv9izY-0PWvCWEWzfqOmzq3jGHIbWCDiwk/edit?usp=sharing | Project Schedule on Google Sheet:https://docs.google.com/spreadsheets/d/1IlT3Na8Ujlv9izY-0PWvCWEWzfqOmzq3jGHIbWCDiwk/edit?usp=sharing | ||
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Latest revision as of 19:29, 25 November 2018
PROJECT POSTER | RESEARCH PAPER | OTHER GROUPS |
Contents
Motivation
Household electricity consumption in Singapore has increased by about 17% over the past decade, according to a report by the National Environment Agency in May 2018. On aggregate levels, Singapore households consumed 7,295 GWh (gigawatt hours) in 2017, which roughly translates to an average expenditure of $1,000 a year on electricity per household. [1]
Electricity consumption is a national issue, especially given that about 95% of Singapore's electricity supply is imported. [2] It is therefore important to encourage households to consume electricity in more sustainable ways.
Traditionally, the lack of transparency surrounding electricity use has been acknowledged as a possible challenge in raising awareness on electricity consumption. [3] Improving visualisation of household electricity consumption can help people in Singapore gain better clarity of their consumption habits and expenditure, and thus more incentive to reduce electricity usage. [3]
Our project visualises the distribution of household electricity consumption across planning regions in Singapore, accounting for type of residential homes, income and demographic profiles. We aim to better communicate electricity consumption in everyday life to people in Singapore, and ultimately engage them to reduce electricity consumption.
Objectives
This project aims to create an interactive data visualisation tool to achieve the following objectives:
- Provide a clear geographical visualisation for household electricity consumption across planning areas
- Provide an overview of the relationship between resident population and electricity consumption for the various housing types across time
- Allow users to explore household electricity consumption in Singapore by comparing across planning regions
- Ensure a smooth interactivity for a better user experience
Data
Datasets | Data Attributes | Rationale of Usage |
---|---|---|
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This dataset contains granular data on average household monthly electricity consumption by postal code for each type of housing between 2013 to 2016. | |
Source: https://www.ema.gov.sg/statistic.aspx?sta_sid=20140617E32XNb1d0Iqa |
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This dataset will be used to visualise the overall historical trend of monthly average household electricity consumption in Singapore from 2005 to 2017. |
Source: https://www.singstat.gov.sg/find-data/search-by-theme/population/geographic-distribution/latest-data |
|
This dataset is used to complement the main dataset by providing detailed information about the number of Singapore residents in each planning area, according to dwelling type.
|
Source: https://data.gov.sg/dataset/hdb-property-information |
|
This dataset provides a comprehensive record of Singapore HDB Property Information, and enables us to scale the average electricity consumption by the number of units for the given dwelling type for the block. |
Source: Real Estate Information System |
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This dataset will be a complementary dataset for private housing types by providing information on the number of units. |
Source: https://www.ura.gov.sg/realEstateIIWeb/resources/misc/list_of_postal_districts.htm |
|
This dataset is used to map the postal sector, also the first two digits of the postal code, to the corresponding postal district. |
Related Works
Related Works | What We Can Learn |
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Prototype
Iteration 1
Landing Page
[1]Logo
[2]Bivariate Choropleth Map
[3]Filter
[4]Button to Historical Trend Page
[5]Slope Graph
Historical Trend Page
[1]Area Chart for Total Electricity Consumption
[2]Area Chart for Number of Singapore Resident
[3]Connected Scatter Plot
[4]Rate of Change of Number of Singapore Resident and Total Electricity Consumption
Application Architecture
Project Schedules
Project Schedule on Google Sheet:https://docs.google.com/spreadsheets/d/1IlT3Na8Ujlv9izY-0PWvCWEWzfqOmzq3jGHIbWCDiwk/edit?usp=sharing
Challenges
Challenges | Possible Solutions |
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Unfamiliarity with D3.js |
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Data merge, cleaning and transformation |
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Choice of web hosting provider |
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Unfamiliar with implementation efforts required for customised D3.js interactivity |
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References
[1] The Straits Times (https://www.straitstimes.com/singapore/singapores-household-electricity-consumption-up-17-per-cent-over-past-decade)
[2] Energy Market Authority (https://www.ema.gov.sg/electricity_market_overview.aspx)
[3] National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4746293/)
[4] Energy Market Authority (https://www.ema.gov.sg/singapore_energy_statistics.aspx)
[5] Data.gov Database (https://data.gov.sg)
[6] D3.js (Documentation https://d3js.org/)
[7] Observale (https://beta.observablehq.com/)
[8] OneMap (https://www.onemap.sg/main/v2/)
[9] Prediction of Buildings Energy Consumption (http://cs109-energy.github.io/building-energy-consumption-prediction.html)
Feedback
Please feel free leave your comments, suggestions or anything interesting :)