Bulk Regression Analysis - Accounts

The 'Bulk Regression Modeller - Accounts' report allows you to perform bulk regression analysis on weather metrics for selected range of Accounts, based on a few pre-defined parameters. The regression analysis in the report works the same way as the incumbent Regression Analysis dashboard on Account, with the added advantage of running for bulk.

The report can be run in two modes - "View Only" mode or "Commit To Save" mode. In 'Commit To Save' mode, regression results for eligible Accounts will be saved into the platform together with relevant HDD and CDD metric changes required for weather normalization reporting. The report can only be run as an email attachment in CSV format in both modes.

Report Prerequisite

To get most out of the report, it is recommended that for each Location included in the reporting selection criteria, the following should be configured prior to running the report:

Location should be linked to a valid weather station with up-to-date weather data ,or sufficient data for the historical period the regression analysis is to be run on
Base HDD and Base CDD temperatures (in Celsius) of the Location - under Location Settings, optional
Non-working days - under Location Settings, optional but recommended if there is any

Report Selection

Similar to any other report in the platform, you can make a range of selections prior to running the report.

Selection	Description
Group	Choose to run for one or all Groups.
Location	Choose to run for one or all Locations.
Data Type	Select a Data Type to run regression analysis on. You have to choose one particular Data Type, e.g. , Electricity [kWh]. Only Accounts belong to the selected Data Type will be included in the report.
Filter By # 1 - Execution Mode	Choose 'View Only' or 'Commit To Save' mode for the report. Default is 'View Only'.
Filter By #2 - Coverage	You have the choice of letting the report to determine the best fit Base HDD and Base CDD temperatures for your Locations, or to pre-supply the base values by yourselves prior to running the report. At the same time, you can also choose to run the regression analysis for Accounts that have not had a regression model set up before, or to run it for all Accounts regardless of whether they have any existing regression models: Auto Fit - Only create new models Auto Fit - Create new or replace existing models Use existing Location Base HDD and Base CDD values - Only create new models Use existing Location Base HDD and Base CDD values - Create new or replace existing models
Filter By #3 - R2 Acceptance Criteria	When running the report in 'Commit To Save' mode, if an Account's R2 regression result value is higher than or equal to the selected accepted R2 value, the model will be saved into the platform. When running the report under 'View Only' mode, these Accounts will be marked as 'To Be Applied'. By default the accepted R2 value is 0.75: R2 >= 0.75 R2 >= 0.7 R2 >= 0.65 R2 >= 0.6 R2 >= 0.55 R2 >= 0.5 No Minimum R2
Duration	Default to 1 Year
Ending With	Choose a calendar month

Report Output

The report performs regression analysis on weather data for all active Accounts included in the report selection range. The regression analysis result, together with the reporting outcome will be shown in the CSV report that comes with the email. Below are the explanations of some of the key columns in the report:

Column	Description
Result	The outcome of running the report: To Be Applied - the regression result meets the R2 acceptance criteria and will be saved into the platform Not Applied - the regression result does not meet the R2 acceptance criteria and will not be saved Skipped - the item didn't go through the regression analysis process because it was deliberately excluded, e.g., it already has an existing model while the report was run for creating new models only, or the report was run for using existing location's base HDD and CDD values but they have not been configured in the system yet Invalid - invalid regression result returned for the item When running the report in 'Commit To Save' mode, the 'To Be Applied' items will be marked as 'Applied' instead.
Active R2, Active_HDD_Base, Active_CDD_Base	The existing regression R2 result, Base HDD and Base CDD values associated with the Account if any. The R2 value is a setting of the Account while Base HDD and Base CDD values are settings of its parent Location.
Model R2, Model_HDD_Base, Model_CDD_Base	The new proposed regression R2 result, best fit Base HDD and Base CDD values associated with the Account after going through the regression analysis.
Status	The result of the regression analysis. Strong Model - R2 value is more than 0.75 Weak Model - R2 value is between 0.5 and 0.75 Invalid Model - R2 value is less than 0.5, or there is no sufficient data to perform the regression analysis
Conflicting Location HDD_CDD Base Values	A warning message will appear here if Accounts in the same Location have different best fit Base HDD and Base CDD values to each other, or any of them is different from its Location's existing Base HDD and Base CDD values. If the report is run in 'Commit To Save' mode, the Model_HDD_Base and Model_CDD_Base values associated with the Account that has the highest R2 result among all Accounts in the same Location will be saved into the Location settings, and it will be used for normalization reporting for all Accounts in the same Location.
Sample_Of_Data	Number of months in which consumption data is present and is used for the regression analysis.
Message	Some additional information about the regression result, such as number of non-working days, unit of temperature, linked weather station etc. A preliminary assessment of HDD and CDD t-statistic result will also be shown here if applicable. This is also the same message you would get if running the same regression analysis using the dashboard.

Closed Accounts

The report excludes any accounts that have been closed at the time of the report ending period. For example, if the report is run for 1 year ending Dec-2018, then any accounts that have been closed in December 2018 or prior, will be excluded and not show up in the report.

Commit To Save

When running the report in 'Commit To Save' mode, the regression result together with relevant HDD and CDD metric changes will be saved into the platform - for Accounts that are being marked as 'To Be Applied' if running the same in 'View Only' mode. Below is a list of things that will be saved or altered in the platform:

Entity	Attributes
Account Settings	Slope for HDD, Slope for CDD, Base Load for Working Day, Base Load for Holiday, R2, Regression Results, Regression Base Period
Location Settings	HDD Base Temperature, CDD Base Temperature
HDD and CDD Stats	HDD and CDD stats for the Location will be recompiled if there is any Account in the Location has its Result equals to 'Applied'. This is a necessity because the calculation of HDD and CDD values relies on the Base HDD and Base CDD values, which could have been changed as part of this exercise.

*The slope or co-efficient values of HDD and CDD will be saved in the form of Celsius - assuming the normalization reporting would use HDD and CDD values in Celsius. Therefore the value saved may be different from the value appearing in the report if it is in Fahrenheit. To get the corresponding Fahrenheit co-efficient, divide the Celsius value by 1.8.

Who can run the report?

The report can be run by both System Administrators and General Users of the platform in 'Commit To Save' mode.

Report Limitation

Like any other regression analysis tool, it is time consuming and computational intensive to perform regression analysis in auto fit mode - the tool is required to try out a large number of combinations of Base HDD and Base CDD values, before it can find a best fit. Generally it is expected that the report will time out if the number of Accounts required for regression analysis exceeds 300.

It is recommended to run the report for a smaller set of data each time, e.g., for a Group only rather than running for the whole Organization. The performance will also be improved substantially if you can pre-determine and supply the Base HDD and Base CDD values for each Location, rather than using the auto fit method.