Improving Data Quality

Transcription

1 Improving Data Quality Rizwaan Sahib, Associate RIG Engineer PIRP Stakeholder Meeting

2 Recap CAISO ran a study on PIRP II Identified false data quality errors. Mainly concentrated with barometric pressure and air temperature. Recommendation was set forth to remove those points from validation. Currently we are going through process of implementing that change. 2 PIRP II Findings

3 Goals Previous study revealed that in a 14 week period 4% of all data was in data quality failure. Generally a good outlook, with minor effects to forecasting in the long run. Goals Improving Forecasts through better Data Quality Improve the accuracy in identifying data quality failures Reduce the overall response time to data quality failures. 3 PIRP II Findings

4 Next Step Perform a more in-depth study in identifying data quality failures. Re-examine current processes in identifying, validating, and reporting data quality errors. Continue to work with Forecast Service Providers (FSP), CAISO personal, Scheduling Coordinators (SC), and site engineers to improve current processes. Improve the accuracy of the data validation process. Reduce the length of time it takes to correct data quality errors. 4 PIRP II Findings

5 Improving Data Quality Reviewing Validation process CAISO Forecasters Make sure we have the same validation procedure Study the root causes of failure Field devices Communication failures Review the communication & compliance process Provide Recommendation 5 PIRP II Findings

6 Data Validation Process Data Validation Process for FSP. Checks for Flatlines If all Meteorological data does not change for over 1 hour. If Wind Speed is greater the 5 m/s and data does not change for over 1 hour. Checks for Communication Failures 6 PIRP II Findings

7 Review Data Validation Process Test the accuracy of the data Validation Process Review data identified through the validation process. Test manually the sensitivity of the validation process. Under reporting data quality failures. Over reporting data quality failures. 7 PIRP II Findings

8 CAISO Data validation Logs Create a weekly log of all Data Quality Failures Flatlines Communication Failures Log root causes of failure Log the time it takes to resolve issue. Time it takes to identify issue. Communicate issue to responsible party Resolve issue. 8 PIRP II Findings

9 Weekly Log Expansion of our current process. Creates an automated report complementing PIRP II. Easier to use in data analysis. 9 PIRP II Findings

10 Identifying Root Cause Create a Log that identifies the root causes of Data Quality Failure. Identifies the main causes of failure Used to provide recommendation to reduce failures 10 PIRP II Findings

11 Issue Logs Issue Logs will archive all data quality issues and identify: Participant Type of problems Time it takes CAISO to identify issue Time it takes to communicate problem to personal who will resolve issue. Time it takes to resolve the issue 11 PIRP II Findings

12 Communication & Compliance Review the Process of how we contact personal to resolve issues. SC needs to looks at PIRP II and identifies data quality failures. They must contact site and relay any issues. CAISO Engineer will look at their historian, or real time displays to identify data quality failures, weekly. Contacts Site and relays information on any issue. Review compliance notification process. Current process is 7 days of continuous data quality failure, compliance send out a warning letter. 12 PIRP II Findings

13 Purpose The Logs will gather data for 6 weeks. May increase the time period if enough data is not gathered. Is not planned to be a permanent process. Will be used to identify problem in our current processes. Used as a basis to provide recommendation to improve CAISO current validation and communication process. 13 PIRP II Findings