Electricity Power Thefts Project in Power Bi
Budget: €30 – €250 EUR
The delivery deadline is on March 25th, 2023
*The delivery deadline is on March 25th, 2023
Given in the attachment are two CSV files containing data with
Consumer Records in groups/categories and
Consumption Historical measurements in groups/categories
By use of the given historical data to prepare a Power Bi template to isolate potential Power thefts under the following method:
Calculate the daily average between power meter measurements, per power meter, group/category, the daily totals, and the percentage of each to the daily relevant totals.
Store the above calculations in a pre-specified 52-week per year that we provide in an excel.
Calculate the Standard Deviation of each power meter consumption in weeks per group/category/totals.
Consider results using the 68-95-99.7 empirical rule.
The Report view is essential and should be clear and easy to use with
Slices and buttons to quickly make any possible combination between groups and categories.
Rank and prioritize the results.
The CSV files fields:
Records _TEST fields
Property ID (ACCT_NBR)
Property habitant serial No (SUCCESSOR)
Voltage (VOLTAGE)
Geographical coordinates X (ACCT_WGS84_X), Altered due to GDPR
Geographical coordinates Y (ACCT_WGS84_X), Altered due to GDPR
Fuse size (PARNO)
Usage type (XRHSH)
Max allowed power capacity (CONTRACT_CAPACITY)
Manually/electronically counted (ACCT_CONTROL)
Consumption _TEST fields
Property ID (ACCT_NBR)
Property habitant serial No (SUCCESSOR)
Consumer meter category (business-home-night meter) (BS_RATE)
Consumer meter No (MS_METER_NBR)
Consumption in KWh-last measure minus previous measure-(CSS_MS_HS_USE)
Measurement Date (MEASUREMENT_DATE)
*The delivery deadline is on March 25th, 2023
Given in the attachment are two CSV files containing data with
Consumer Records in groups/categories and
Consumption Historical measurements in groups/categories
By use of the given historical data to prepare a Power Bi template to isolate potential Power thefts under the following method:
Calculate the daily average between power meter measurements, per power meter, group/category, the daily totals, and the percentage of each to the daily relevant totals.
Store the above calculations in a pre-specified 52-week per year that we provide in an excel.
Calculate the Standard Deviation of each power meter consumption in weeks per group/category/totals.
Consider results using the 68-95-99.7 empirical rule.
The Report view is essential and should be clear and easy to use with
Slices and buttons to quickly make any possible combination between groups and categories.
Rank and prioritize the results.
The CSV files fields:
Records _TEST fields
Property ID (ACCT_NBR)
Property habitant serial No (SUCCESSOR)
Voltage (VOLTAGE)
Geographical coordinates X (ACCT_WGS84_X), Altered due to GDPR
Geographical coordinates Y (ACCT_WGS84_X), Altered due to GDPR
Fuse size (PARNO)
Usage type (XRHSH)
Max allowed power capacity (CONTRACT_CAPACITY)
Manually/electronically counted (ACCT_CONTROL)
Consumption _TEST fields
Property ID (ACCT_NBR)
Property habitant serial No (SUCCESSOR)
Consumer meter category (business-home-night meter) (BS_RATE)
Consumer meter No (MS_METER_NBR)
Consumption in KWh-last measure minus previous measure-(CSS_MS_HS_USE)
Measurement Date (MEASUREMENT_DATE)