Charge Metrics Proposal

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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:

1. Provide a clear geographical visualisation for household electricity consumption across planning areas
   
2. Provide an overview of the relationship between resident population and electricity consumption for the various housing types across time
   
3. Allow users to explore household electricity consumption in Singapore by comparing across planning regions
   
4. Ensure a smooth interactivity for a better user experience
   

Data

Datasets	Data Attributes	Rationale of Usage
EMA Household Energy Consumption Source: https://www.ema.gov.sg/statistic.aspx?sta_sid=20140617E32XNb1d0Iqa	Postal Code Type of Dwelling Month, 2013-2016 Electricity Consumption	This dataset contains granular data on average household monthly electricity consumption by postal code for each type of housing between 2013 to 2016.
Average Monthly Household Electricity Consumption by Dwelling Type, 2005-2017 Source: https://www.ema.gov.sg/statistic.aspx?sta_sid=20140617E32XNb1d0Iqa	Type of Dwelling Average Monthly Electricity Consumption (in kWh) Date (mmm-yyyy)	This dataset will be used to visualise the overall historical trend of monthly average household electricity consumption in Singapore from 2005 to 2017.
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	Planning Area Type of Dwelling Resident Population Year	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.
HDB Property Information Source: https://data.gov.sg/dataset/hdb-property-information	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 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.
Private Apartment Information Source: Real Estate Information System	Block Number Street Residential No of Units Property Type Postal District Postal Sector Postal Code Planning Region Planning Area	This dataset will be a complementary dataset for private housing types by providing information on the number of units.
List of Postal Districts Source: https://www.ura.gov.sg/realEstateIIWeb/resources/misc/list_of_postal_districts.htm	Postal District Postal Sector General Location	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
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/	Interactive dashboard allows the user to view the top ranking planning regions in terms of energy consumption, as well as choose the number of top ranking planning regions they are interested in. The use of a filter dashboard action also allows the user to view information only for selected planning regions.
Prediction of Buildings Energy Consumption Source: http://cs109-energy.github.io/building-energy-consumption-prediction.html	Calendar view map allows the user to view the daily pattern of energy consumption, and detect any hourly patterns easily, if present. Provide multiple possible machine learning models for the prediction on energy consumption. Direct illustration of graphs and models on python Jupyter notebook for effective reproducible communication.
Visualising Energy Consumption in Philadelphia Source: http://www.kennethelder.com/visualizing-energy-consumption-in-philadelphia/	Allows the user to hide or view the scatter plot without leaving the landing page or changing the map view. Measures for heat map and scatter plot can also be customised by the user. Project incorporated an added dimension to their scatter plot by sizing the points by the energy rating. This is useful for adding another dimension on top of the two variables typically represented on a scatter plot.
Visualising U.S. Energy Consumption in One Chart Source: http://www.visualcapitalist.com/visualizing-u-s-energy-consumption-one-chart/	Clear overview of how different energy sources are used and classified Good usage of colours to clearly illustrate individual flow of the energy source

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 Resident
[3]Connected Scatter Plot
[4]Rate of Change of Number of Singapore Resident and Total Electricity Consumption

Project Schedules

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

Challenges

Challenges	Possible Solutions
Unfamiliarity 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
Unfamiliar 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] 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

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