Difference between revisions of "ELECgrid Proposal"
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| − | [[ELECgrid_Proposal|<font color="#000"><strong> | + | [[ELECgrid_Proposal|<font color="#000"><strong>PROJECT DETAILS</strong></font>]] |
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[[ELECgrid_Research_Paper|<font color="#111"><strong>RESEARCH PAPER</strong></font>]] | [[ELECgrid_Research_Paper|<font color="#111"><strong>RESEARCH PAPER</strong></font>]] | ||
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| + | [[ELECgrid_About_Us|<font color="#111"><strong>ABOUT US</strong></font>]] | ||
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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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| + | <div style="background: #f4d800; padding: 20px; line-height: 0.3em; letter-spacing:0.1em; font-size:150%; font-weight:bold; text-align: center; margin-left: 5em; margin-right: 5em;"><font color=#000 face="Tw Cen MT">PROJECT OBJECTIVE</font></div> | ||
| + | <div style= "text-align: justify; margin-left: 10em; margin-right: 10em; font-size: 18px;"> | ||
| + | To reduce electricity retailers’ cost and wasted resources by accurately estimating the total monthly electricity consumption per subzone. | ||
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| − | + | <div style="background: #f4d800; padding: 20px; line-height: 0.3em; letter-spacing:0.1em; font-size:150%; font-weight:bold; text-align: center; margin-left: 5em; margin-right: 5em;"><font color=#000 face="Tw Cen MT">PROJECT MOTIVATION</font></div> | |
| − | <div style="padding: 20px; line-height: 0.3em; letter-spacing:0.1em; font-size:150%; font-weight:bold; text-align: | + | <div style= "text-align: justify; margin-left: 10em; margin-right: 10em; font-size: 18px;"> |
| − | <div style= "text-align: | + | 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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</div> | </div> | ||
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| + | <div style="background: #f4d800; padding: 20px; line-height: 0.3em; letter-spacing:0.1em; font-size:150%; font-weight:bold; text-align: center; margin-left: 5em; margin-right: 5em;"><font color=#000 face="Tw Cen MT">PROJECT MILESTONES</font></div> | ||
| + | [[File:Screenshot_2019-03-05_at_12.19.00.png|frameless|center|1300px]] | ||
| + | <!-- End of Project Milestones---> | ||
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|-style="font-size: 130%; | |-style="font-size: 130%; | ||
| + | ! <b>Label</b> | ||
! <b>Data Set</b> | ! <b>Data Set</b> | ||
! <b>Format</b> | ! <b>Format</b> | ||
! <b>Attributes</b> | ! <b>Attributes</b> | ||
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| Average Monthly Household Electricity Consumption Jan- June 2016 | | Average Monthly Household Electricity Consumption Jan- June 2016 | ||
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*Dwelling type | *Dwelling type | ||
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| Average Monthly Household Electricity Consumption Jul- Dec 2016 | | Average Monthly Household Electricity Consumption Jul- Dec 2016 | ||
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| − | * | + | | Singapore Residents by Subzone and Type of Dwelling June 2016 |
| + | | shp | ||
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| + | *Population living in subzone (auxiliary variable) | ||
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| − | * | + | | Subzone_HDB_Postal |
| + | | shp | ||
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| + | *Number of units/dwelling type/postal code | ||
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| + | <div style="background: #f4d800; padding: 20px; line-height: 0.3em; letter-spacing:0.1em; font-size:150%; font-weight:bold; text-align: center; margin-left: 5em; margin-right: 5em;"><font color=#000 face="Tw Cen MT">TECHNIQUES USED</font></div> | ||
| + | <div style= "text-align: justify; margin-left: 10em; margin-right: 10em; font-size: 18px"><font color=#000 face="Tw Cen MT"> | ||
| + | 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 | ||
| + | </div> | ||
| + | <!-- End of Project Description---> | ||
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Latest revision as of 14:22, 14 April 2019
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
PROJECT PROTOTYPE
DATA SOURCES
| Label | Data Set | Format | Attributes |
|---|---|---|---|
| elec1 | Average Monthly Household Electricity Consumption Jan- June 2016 | xls |
|
| elec2 | Average Monthly Household Electricity Consumption Jul- Dec 2016 | xls |
|
| Singapore Residents by Subzone and Type of Dwelling June 2016 | shp |
| |
| Subzone_HDB_Postal | shp |
|
TECHNIQUES USED
1. Small Area Estimate (SAE) - SAE is a statistical technique which involves estimating parameters for small sub-populations.
2. R Shiny Applications
