Integrate automated QA/QC tools directly within the eTRM
QA/QC tools integrated within the eTRM measure development workflow platform that support these potential features:
Measure Data Validation. Check measure package permutation data for completeness, accuracy, compliance, and alignment
Characterization Review. Check characterization content for completeness, accuracy, compliance, and alignment with applicable permutation data and requirements
Permutation Optimization: Apply streamlining algorithms to recommend optimized permutations for new measures and version updates.
Claim and Field Data QC: Use measure property data to support claim validation and field data verification.
Business rationale:
Improve measure package data quality, reduce manual effort, and fast-track the measure development/review process through automated data QC tools.
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Comments13
An Anonymous User
May 10
An Anonymous User
May 10
An Anonymous User
May 10
•Merged request
•1 vote
Advance the OfferingID only when the OfferingDesc has a meaningful update
Only advancing the OfferingID when OfferingDesc has a meaningful update. As I understand it, the current practice is to advance the OfferingID whenever the MeasDescription text changes from one measure package version to the next.
For PY026 measure package, updates to MeasImpactType and DelivType fields should not result in new OfferingIDs for offerings that are otherwise the same.
An Anonymous User
May 10
•Merged request
•1 vote
Prevent inputs into certain fields based on the selected MeasAppType
Please consider preventing inputs into:
PreDesc, PreTechGroup, PreTechType, and PreTechID fields when MeasAppType is either the NR or NC.
StdDesc, StdTechGroup, StdTechType, and StdTechID fields when MeasAppType is either AOE, BW, BRO-Beh, BRO-RCx, or BRO-Op.
CA eTRM
May 11
•Merged request
•1 vote
Automatically sort measure parameters and value tables into a preset order.
Cal TF staff has convention for the ordering of parameters and value tables. This enhancement automatically sorts the measure parameters and value tables to the preset order, ensuring compliance with QC guidelines and lessen the burden on measure developers. Measure developers can reorder the list if desired.
Alternative: QA/QC guidelines notify measure developers if the objects are out of order and suggest the ordering.
CA eTRM
May 11
•Merged request
•1 vote
Make the "data spec non-conformance" error message more visible and informative
Make the "This permutation configuration does not conform to the CalTF’s standardized data spec" error message visible to all measure contributor roles and even when there isn’t an open draft. Currently, this error message is only visible to a measure developer when they have the draft open.
Should not be able to move forward with MP if there is this issue.
Also, in the error message list the fields that are not mapped correctly OR highlight those fields in the configuration panel.
CA eTRM
May 12
Quality check to ensure that Measure Detail ID is unique
CA eTRM
Jun 15
Load Shape Verification, possibility
TechID is unique to UEC
Electric Impact Profile ID is a valid Avoided Cost Combo (may need some intergration with CEDARS to get the list of uploaded avoided cost combo)
UES = UECBase - UECMeas
CA eTRM
Oct 14
•Merged request
•1 vote
Add visual cues for unmapped permutation data spec fields
In the permutation fields configuration panel, highlight unmapped fields (e.g., in red) to help measure developers quickly identify and correct the “This permutation configuration does not conform to the California Technical Forum’s standardized data spec.“ error message.
CA eTRM
Oct 14
•Merged request
•1 vote
Automated Permutation QC Tool
Description
Implement a configurable, rules-based QC check that validates structured measure permutation data using an explicit, configurable ruleset.
Rules:
Incomplete data
Incorrect data type and value
Invalid parameter combinations
Mathematical inconsistency
Business and cross-systems rules
Missing or duplicate permutation records
Requirements:
Deterministic outputs (e.g., rules are applied correctly, consistently, objectively)
Seamless integration with existing measure development and review workflow. The validation process is automatically run during permutation building or manually triggered by measure developers
Actionable error messages that identify the affected field or permutation
Clear explanation of the validation failure and source of the rules, enabling measure developers to efficiently correct issues before submission or review
Transparency to the source of the rule, enabling measure developers to assess its applicability and make informed judgments
QC rules should be configurable and maintainable as data specifications evolve.
Business rationale
Improve measure package data quality and systems resiliency
Success Metrics
Fewer post-publishing corrections of critical data errors
Fewer CPUC QC comments per measure package.
Shorter time from submission to approval.
Fewer downstream system integration failures (e.g. CET) due to data mismatch