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Time series anomaly
detection and data
imputation in energy field
➢ GRDF delivers natural gas through
Europe's largest distribution network
✓
✓
✓
✓
✓
✓
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GRDF’s Mission
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DataLab
•
•
Answer business use cases by
leveraging Data Science & AI
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Main Temporal Data Sources
FORECASTING
PREDICTIVE
MAINTENANCE
NETWORK SIZING
Achieve Good Data Quality
➢ Data Quality is crucial for business use cases implementation.
Model efficiency entirely depends on it.
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Challenges of Anomaly Detection
Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
7
Objectives of Anomaly Detection
Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
8
SOTA
•
•
•
•
8
Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
9
SOTA
•
•
•
•
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
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Methodology (1)
➢
➢
+
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
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Methodology (2)
•
•
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
•
[Costa et al, 2017]
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Results
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
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Results
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
14
Classic Methods
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
15
Method in Production
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
16
Problem Faced
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
➢ What happens when we don’t have billing data anymore? The gaz
pressure example:
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Solution
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
18
Comparison with Previous Imputation
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection
19
Key Points
• Clearly define the scope and the challenges
• trade-off between performance and resources
• Importance of DQ in industrial systems
20
Key Points
• Clearly define the scope and the challenges
• trade-off between performance and resources
• Importance of DQ in industrial systems
➢ We’re hiring

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Anomaly detection and data imputation within time series