Difference between revisions of "ELECgrid Proposal"

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[[ELECgrid_Proposal|<font color="#000"><strong>PROPOSAL</strong></font>]]
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[[ELECgrid_Proposal|<font color="#000"><strong>PROJECT DETAILS</strong></font>]]
  
 
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As we speak, Singapore is rolling out its plan for the privatisation of the electricity market. As of now, there are as many as 12 electricity retailers competing to sell their energy package and each retailer charges a price lower than the tariff price set by Singapore Power, the de facto energy retailer. These retailers purchase electricity in bulk from electricity-generating companies instead of producing their own, and subsequently sell the resource to their customers.
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Singapore is rolling out its plan for the privatisation of the electricity market. Currently, there are as many as 12 electricity retailers who typically charge a price lower than that set by Singapore Power – the de facto energy retailer. These retailers purchase electricity in bulk from electricity generating companies. At the moment, electricity retailers are unable to forecast electricity consumption accurately with the kind of data available publicly (average electricity consumption per postal code). This inevitably means that retailers are not buying a close enough amount of electricity to meet the actual electricity demand of their customers. Resources and potential revenue are therefore being wasted and lost because of this. This project therefore utilises Small Area Estimate to improve accuracy of forecasting by combining the already available data on average electricity consumption per postal code and other auxiliary information.  
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One of the challenges faced by these retailers is the lack of accurate demand forecast for electricity. This is a key issue as a poor forecast of demand for electricity results in the resource being wasted and revenue lost for the company.
 
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Our project therefore aims to estimate the total monthly electricity consumption per housing units to provide these electricity retailers a picture of how much electricity is needed in the grid.
 
 
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To provide an estimate of the monthly electricity consumption by dwelling type.
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To reduce electricity retailers’ cost and wasted resources by accurately estimating the total monthly electricity consumption per subzone.
  
 
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The topic on the privatisation of the electricity market stirred our curiosity and we decided to looked at some of the challenges faced by the private electricity retailers. Through our discussions,we realised the importance of a more robust forecast for electricity demand. Hence using the knowledge of geospatial analytics, we would like to tackle this issue.
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Ultimately, cost-savings for retailers would be passed onto the consumers. This is of great opportunity to look into as various consumers even our families are shifting into private-provided electricity.  
 
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! <b>Label</b>
 
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*Dwelling type
 
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| Average Monthly Household Electricity Consumption Jul- Dec 2016
 
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*Subzone boundary
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| Singapore Residents by Subzone and Type of Dwelling June 2016
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*Population living in subzone (auxiliary variable)
 
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*Number of units/dwelling type/postal code
 
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1. Small Area Estimate (SAE) - SAE is a statistical technique which involves estimating parameters for small sub-populations.
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2. R Shiny Applications
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Latest revision as of 14:22, 14 April 2019

Screenshot 2019-03-05 at 13.51.50.png

PROJECT DETAILS

POSTER

PROJECT APPLICATION

RESEARCH PAPER

ABOUT US


PROJECT DESCRIPTION

Singapore is rolling out its plan for the privatisation of the electricity market. Currently, there are as many as 12 electricity retailers who typically charge a price lower than that set by Singapore Power – the de facto energy retailer. These retailers purchase electricity in bulk from electricity generating companies. At the moment, electricity retailers are unable to forecast electricity consumption accurately with the kind of data available publicly (average electricity consumption per postal code). This inevitably means that retailers are not buying a close enough amount of electricity to meet the actual electricity demand of their customers. Resources and potential revenue are therefore being wasted and lost because of this. This project therefore utilises Small Area Estimate to improve accuracy of forecasting by combining the already available data on average electricity consumption per postal code and other auxiliary information.

PROJECT OBJECTIVE

To reduce electricity retailers’ cost and wasted resources by accurately estimating the total monthly electricity consumption per subzone.

PROJECT MOTIVATION

Ultimately, cost-savings for retailers would be passed onto the consumers. This is of great opportunity to look into as various consumers even our families are shifting into private-provided electricity.


PROJECT MILESTONES
Screenshot 2019-03-05 at 12.19.00.png



PROJECT PROTOTYPE
Screen Shot 2019-03-05 at 11.29.44 AM.png


DATA SOURCES
Label Data Set Format Attributes
elec1 Average Monthly Household Electricity Consumption Jan- June 2016 xls
  • Average electricity consumption
  • Dwelling type
elec2 Average Monthly Household Electricity Consumption Jul- Dec 2016 xls
  • Average electricity consumption
  • Dwelling type
Singapore Residents by Subzone and Type of Dwelling June 2016 shp
  • Population living in subzone (auxiliary variable)
Subzone_HDB_Postal shp
  • Number of units/dwelling type/postal code


TECHNIQUES USED

1. Small Area Estimate (SAE) - SAE is a statistical technique which involves estimating parameters for small sub-populations.

2. R Shiny Applications


Retrieved from ‘https://wiki.smu.edu.sg/1819t2is415/index.php?title=ELECgrid_Proposal&oldid=2230’