Group06 Elec3city Proposal

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PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

Project Motivation

When it comes to the government’s push for efficient energy usage, most effort is expended on the efficiency of energy sources – e.g. using less carbon-intensive fuels (https://www.nea.gov.sg/our-services/climate-change-energy-efficiency/energy-efficiency/energy-efficient-singapore). However, hitherto, there has been scant statistical analysis on possible causes of inexpedient energy usage by households, with consideration of their varied age structure and the geospatial variation of environmental conditions (e.g. temperature’s effect on energy consumption).

Our team sees geospatial analytical tools (such as R) as thus far largely unexploited in exploring the origins of geospatial variation in energy consumption and is thus using spatial interpolation techniques (such as kriging) to provide an app which allows for authorities in Singapore such as the National Environment Agency to understand with data-driven evidence the origins of variation in Singapore household energy consumption so as to have more targeted efforts to reduce energy wastage.

Project Objective

Through our project, we aim to:

Deliver a dynamic (interactive) application that provides authorities such as the National Environment Agency and Housing Development Board with the ability to:

• View monthly and yearly temperature geospatial variation in Singapore, and compare that to energy consumption at building level granularity
• View housing composition (e.g. age, income and race) geospatial variation, and compare that to energy consumption at a building level granularity

so as to make data-informed, targeted decisions to promote reduction of energy usage among varying types of households in Singapore, where hitherto there has been a blanket approach.

Data

Data	Source	Data Type
Average Monthly Household Electricity Consumption by Postal Code (Public Housing) & Dwelling Type, 2H 2016	Energy Market Authority (ema.gov.sg)	xls
Average Monthly Household Electricity Consumption by Postal Code (Public Housing) & Dwelling Type, 1H 2016	Energy Market Authority (ema.gov.sg)	xls
Average Monthly Household Electricity Consumption by Postal Code (Public Housing) & Dwelling Type, 2H 2015	Energy Market Authority (ema.gov.sg)	xls
Average Monthly Household Electricity Consumption by Postal Code (Public Housing) & Dwelling Type, 1H 2015	Energy Market Authority (ema.gov.sg)	xls
Average Monthly Household Electricity Consumption by Postal Code (Public Housing) & Dwelling Type, 2H 2014	Energy Market Authority (ema.gov.sg)	xls
Average Monthly Household Electricity Consumption by Postal Code (Public Housing) & Dwelling Type, 1H 2014	Energy Market Authority (ema.gov.sg)	xls
Average Monthly Household Electricity Consumption by Postal Code (Public Housing) & Dwelling Type, 2H 2013	Energy Market Authority (ema.gov.sg)	xls
Average Monthly Household Electricity Consumption by Postal Code (Public Housing) & Dwelling Type, 1H 2013	Energy Market Authority (ema.gov.sg)	xls
Average Monthly Household Electricity Consumption by Postal Code (Private Apartments), 2015 and 2016	Energy Market Authority (ema.gov.sg)	xls
Average Monthly Household Electricity Consumption by Postal Code (Private Apartments), 2013 to 2014	Energy Market Authority (ema.gov.sg)	xls
Resident Households by Planning Area and Dwelling Type/Household Size/Monthly Household Income	Department of Statistics Singapore (singstat.gov.sg)	xls
Singapore Residents by Planning Area/Subzone, Age Group and Sex, June 2000 - 2018	Department of Statistics Singapore (singstat.gov.sg)	csv
Singapore Residents by Planning Area/Subzone and Type of Dwelling, June 2000 - 2018	Department of Statistics Singapore (singstat.gov.sg)	csv
Singapore Climate Historical Data - crawled to get temperature and rain data from 2013 to 2016 at daily granularity	Meteorological Service Singapore (weather.gov.sg)	csv

Literature Review

Methodology:

Learning Points:

Areas for improvement:

Approach

Techniques:

Web Application Design

Design Inspiration

The dashboard design is inspired by https://stanleyadion.shinyapps.io/AmazeingCrop

Initial Storyboard

	Design	Description
1.		Project and Dataset Overview
2.		Bivariate Choropleth Maps showing relationships between energy consumption with other factors Users can choose the factor they want to compare with energy consumtion
3.		A Box-plot showing distributions of energy consumption by Planning Zone and Dwelling Type
4.		Lisa Maps showing spatial clustering of energy consumption observations
5.		Overview of Data for GWR model
6.		Transform Data for GWR model Users can use a histogram to check whether the variable is normally distributed
7.		Select Variables for GWR model Users can remove correlated variables with the help of the correlation matrix plot
8.		Configure a GWR model and view the results

Project Challenges

	Key Challenges	Description	Solution
1.	Technical Challenge	The proposed geovisualization including bivariate choropleth maps and box-plots by planning zones and dwelling type is complex. The group may encounter technical challenges such as finding relevant R packages and functions when building the visualization.	Research on relevant R tutorials Consult Prof Kam
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Project Timeline

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