Charge Metrics Proposal

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PROPOSAL

PROJECT POSTER

PROJECT APPLICATION

RESEARCH PAPER

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.

Electricity consumption is a national issue, especially given that Singapore has finite energy sources. 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. 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.

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.

Data

Datasets	Data Attributes	Rationale Of Usage
EMA Household Energy Consumption Source: https://www.ema.gov.sg/Statistics.aspx	Postal Code Type of Dwelling Month, 2013-2016 Electricity Consumption	There are 2 group of dataset. 1. Household Energy consumption
Singapore Residents by Planning Area and Type of Dwelling, 2000 - 2017 Source: https://www.singstat.gov.sg/find-data/search-by-theme/population/geographic-distribution/latest-data 500px	Planning Area Type of Dwelling Resident Population Year	This dataset aims to complement the main dataset by providing detailed information about the latitude and longitude of the bus stops located around HDB. We use a javascript geocoding script to convert all the X and Y coordinates to EPSG:4326 latitude and longitude coordinates.
HDB Property Information Source: https://data.gov.sg/dataset/hdb-property-information 500px	Block Number Street Residential Total Dwelling Units Number of 1-room Sold Number of 2-rooms Sold Number of 3-rooms Sold Number of 4-rooms Sold Number of 5-rooms Sold Number of 5-rooms Sold Number of Executive Condominiums Sold	This dataset aims to complement the main dataset by providing detailed information about the latitude and longitude of the MRT stations located around HDB. We use a javascript geocoding script to convert all the X and Y coordinates to EPSG:4326 latitude and longitude coordinates
Master Plan Subzone Boundary Names and GeoPolygon (https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-no-sea) 500px 500px	Polygon Name Subzone Number Subzone Code Region Name Area Code Area Indicator	This dataset aims to complement the main dataset by providing detailed information about all the subzone in Singapore. We use a javascript library toGeoJson.js to help us convert .KML file to .GeoJson file

Related Works

Related Works	What We Can Learn
Dashboard Visualisation of Average Monthly Household Energy Consumption Per Year in Singapore Source: https://analyticsandintelligentsystems.wordpress.com/2017/04/28/dashboard-visualisation-of-average-monthly-household-energy-consumption-per-year-in-singapore/
Prediction of Buildings Energy Consumption Source: http://cs109-energy.github.io/building-energy-consumption-prediction.html
Visualizing Energy Consumption in Philadelpia Source: http://www.kennethelder.com/visualizing-energy-consumption-in-philadelphia/
Visualizing U.S. Energy Consumption in One Chart Source: http://www.visualcapitalist.com/visualizing-u-s-energy-consumption-one-chart/

Prototype

Landing Page

[1]Logo
[2]Bivariate Chloropleth 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 Residents
[4]Rate of Change of Number of Singapore Residents and Total Electricity Consumption
[3]Connected Scatter Plot

Project Schedules

Project Schedule on Google Sheet:https://docs.google.com/spreadsheets/d/1IlT3Na8Ujlv9izY-0PWvCWEWzfqOmzq3jGHIbWCDiwk/edit?usp=sharing

Challenges

Challenges	Possible Solutions
Unfamiliar with D3.js	Independent learning through online learning resources Validating learning outcome through review and coding practices
Data Merge, Cleaning and Transformation	Planning area electricity consumption data: Since the data provided by Energy Market Authority is an average of type of dwelling for each block, we will scale the average electricity consumption by the number of units for the given dwelling type for the block, then sum all the blocks in the relevant planning area to get the aggregate monthly consumption per planning area. Missing NA records: Due to privacy concerns, Energy Market Authority does not disclose some data points. We will examine the effect of removing the NA records to decide the appropriate action to take.
Choice of web hosting provider	A quick production pipeline required due to the time limit Examine the requirements of the data visualisation: dynamic or static Current solution is to use Github Page as a hosting provider as there is no dynamic data retrieval required
Unfamilar with implementation efforts required for customised D3.js interactivity	We will be spending 2 weeks to familiarise ourselves with D3.js structure and syntax The following 2 weeks will involve us trying out the customised D3.js interactivity The project scope and plan will be re-examined based on the project objective, complexity and time available

References

[1] Energy Market Authority (https://www.ema.gov.sg/singapore_energy_statistics.aspx)
[2] Data Gov Database (https://data.gov.sg)
[3] D3.js (Documentation https://d3js.org/)
[4] Observalehq (https://beta.observablehq.com/)
[5] One Map (https://www.onemap.sg/main/v2/)
[6] Energy Consumption Predition Example (http://cs109-energy.github.io/building-energy-consumption-prediction.html)

Feedback

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