In the energy sector, the use of temporal data stands as a pivotal topic. At GRDF, we have developed several methods to effectively handle such data. This presentation will specifically delve into our approaches for anomaly detection and data imputation within time series, leveraging transformers and adversarial training techniques.

1. Time series anomaly
detection and data
imputation in energy field

2. ➢ GRDF delivers natural gas through
Europe's largest distribution network
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GRDF’s Mission

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DataLab
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Answer business use cases by
leveraging Data Science & AI

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Main Temporal Data Sources
FORECASTING
PREDICTIVE
MAINTENANCE
NETWORK SIZING

5. 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

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Objectives of Anomaly Detection
Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection

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SOTA
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection

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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
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[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

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Classic Methods
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection

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Method in Production
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection

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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

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Comparison with Previous Imputation
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Data Imputation: Classic
Approach
Data Imputation: Bi-
directionnal Approach
Anomaly Detection

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Key Points
• Clearly define the scope and the challenges
• trade-off between performance and resources
• Importance of DQ in industrial systems

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Key Points
• Clearly define the scope and the challenges
• trade-off between performance and resources
• Importance of DQ in industrial systems
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