Using AI in ETL Processes for Electric Distribution Companies

The electric distribution sector is facing an unprecedented increase in data with the widespread adoption of smart grids. Data from sources such as smart meters (AMI), SCADA systems, geographic information systems (GIS), and advanced distribution management systems (ADMS) are leading businesses to generate massive volumes and varieties of data, often referred to as “big data” (The Big Data Problem For Utilities | Camus Energy) (The Big Data Problem For Utilities | Camus Energy). For example, measurements taken every 15 minutes from millions of smart meters and real-time data flowing from sensors and devices in the distribution network make data management for electric distribution companies extremely complex. ETL (Extract, Transform, Load) processes play a critical role in generating value and extracting insights from this data. ETL pulls raw data from different sources, transforms it, and loads it into central data warehouses or analytics systems. However, traditional ETL processes face various challenges in meeting the scale, speed, and flexibility required by electric distribution companies (Building ETL Pipelines with AI | Informatica) (Building ETL Pipelines with AI | Informatica). This study analyzes in depth the challenges faced by electric distribution companies related to big data and examines the role and limitations of ETL processes in overcoming these challenges. It will also compare AI-based ETL automation approaches, including large language models (LLMs), AutoML, and reinforcement learning (RL), to provide a framework for implementing high-level ETL solutions with minimal human intervention in the electric distribution sector.

Big Data Sources and Challenges in Electric Distribution Companies

Electric distribution companies collect data from a wide variety of systems to monitor and optimize grid operations. The primary data sources are as follows (The Big Data Problem For Utilities | Camus Energy) (The Big Data Problem For Utilities | Camus Energy):

SCADA (Supervisory Control and Data Acquisition): Provides real-time measurements of voltage, current, and power from substations and distribution lines, generating critical data about the network’s current state.
ADMS/DMS (Advanced Distribution Management System): Used for fault management, load flow, and voltage profile optimization; includes operational records and status information of network equipment (The Big Data Problem For Utilities | Camus Energy).
GIS (Geographic Information System): Stores geographical locations of network assets and their interconnections, containing topological data such as which transformer is connected to which line (The Big Data Problem For Utilities | Camus Energy).
AMI (Advanced Metering Infrastructure): Collects customer-specific energy consumption data from millions of smart meters, usually every 15 minutes (The Big Data Problem For Utilities | Camus Energy). This data is used for billing, theft detection, and demand management.
DERMS (Distributed Energy Resource Management System): Monitors the current production/consumption status and capacities of distributed energy resources (e.g., solar panels, batteries) (The Big Data Problem For Utilities | Camus Energy).

When data from these various systems are combined, they can provide a comprehensive view of the distribution network’s current status and future behavior (The Big Data Problem For Utilities | Camus Energy). However, if the data remains in data silos, this potential cannot be realized. Unfortunately, many distribution companies today have these systems operating in isolation, making it difficult to integrate the data into a holistic view (The Big Data Problem For Utilities | Camus Energy). This leads to the problem of inconsistent and incomplete information for decision-makers. Many teams spend significant time and effort manually cleaning and integrating data from different sources (The Big Data Problem For Utilities | Camus Energy). In today’s rapidly changing energy sector, the challenge of processing such a large volume of data with limited human resources can hinder network awareness and operational improvements (The Big Data Problem For Utilities | Camus Energy).

Another critical issue in the big data environment is data integrity and quality. There may be format and terminology inconsistencies between data from different systems. For example, the identity of a customer or transformer may be represented by different codes in SCADA, GIS, and maintenance management systems ( Is the Common Information Model Your Solution to Utility Data Problems? ) ( Is the Common Information Model Your Solution to Utility Data Problems? ). These inconsistencies make data integration difficult and can lead to errors. Standards like CIM (Common Information Model), which are widely used in the energy distribution sector, aim to address this problem by providing a common language for different systems to communicate. CIM helps ensure that data like address or customer identity is understood uniformly across systems ( Is the Common Information Model Your Solution to Utility Data Problems? ) ( Is the Common Information Model Your Solution to Utility Data Problems? ). However, when CIM is not implemented or only partially implemented, it requires significant ETL effort for data transformation and mapping. Additionally, the quality of the data is a major concern: raw data collected by distribution companies often contains faulty readings, missing values, or duplicates. For example, some meter values may be missing due to communication errors or inconsistencies in maintenance records entered by field teams. If these missing, erroneous, or duplicate records are not identified and corrected during the ETL process, the resulting analyses may not be reliable (5 Challenges of Data Integration (ETL) and How to Fix Them | Datavail). Therefore, ETL processes must rigorously perform validation, cleaning, and merging steps to ensure data quality.

Critical Data Processing Needs: Anomaly Detection, Predictive Maintenance, and Grid Management

The effective use of big data by electric distribution companies is critical in several application areas related to operational excellence and grid reliability:

Anomaly Detection: The early identification of events that could be considered anomalies in the smart grid is necessary to prevent technical failures and combat electricity theft and cyberattacks. Anomaly types in the distribution network are diverse: unexpected measurement values (e.g., an unusual increase or decrease in a meter’s consumption profile), abnormal fluctuations in network parameters, data integrity attacks (e.g., manipulation of meter data), unauthorized access attempts, or uncontrolled changes in the network model. AI applications for anomaly detection, particularly in theft detection and cybersecurity, are becoming increasingly widespread. As smart grid infrastructure grows, the risk of cyberattacks increases, making it crucial to detect anomalies related to data integrity (e.g., fake data, unauthorized device commands) in real-time (A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence) (A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence). To detect anomalies, large amounts of sensor and measurement data must be processed to learn the normal behavior patterns. This requires ETL processes to combine time series from various sources (e.g., meter readings, voltage-current monitoring, device temperature sensors), clean the data, and feed it into analytical models. Studies have shown that various AI techniques, from linear regression to deep learning, and from support vector machines to graph-based algorithms, have been applied to capture anomalies in the smart grid (A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence). Given the very high volume and diversity of data (e.g., multiple sensor streams for each transformer station, thousands of customer measurements), success in this area requires a robust big data pipeline.
Predictive Maintenance: Proactively maintaining electric distribution assets (e.g., transformers, circuit breakers, lines) before they fail is a critical strategy for reducing outage durations and maintenance costs. Predictive maintenance approaches attempt to identify equipment most likely to fail by analyzing sensor data from the equipment, historical failure records, and environmental data (Predictive Maintenance for Distribution System Operators in Increasing Transformers’ Reliability) (Predictive Maintenance for Distribution System Operators in Increasing Transformers’ Reliability). For example, models can be developed to predict which transformers are at risk of failure by utilizing data such as oil temperature, load flow, voltage fluctuations, and historical maintenance history (Predictive Maintenance for Distribution System Operators in Increasing Transformers’ Reliability). One study demonstrated that a machine learning model trained on transformer data from Colombia could successfully detect transformers at high risk of failure (Predictive Maintenance for Distribution System Operators in Increasing Transformers’ Reliability). Such an application may require collecting 10 years of historical data from more than 10 sources (Predictive Maintenance for Electric Grid – C3 AI) – for example, online sensor measurements, maintenance reports, weather records, and load changes should all be analyzed together. This makes it necessary for ETL processes to integrate large volumes of data, synchronize time series, and fill in missing data appropriately. Data timeliness is also important in predictive maintenance; performing continuous risk calculations with real-time monitoring requires ETL to operate in periodic or continuous streaming mode.
Grid Management and Optimization: Operators need to have a holistic view of the grid’s status at all times for efficient, stable, and secure operation. Grid management includes numerous subfields, such as state estimation, load forecasting, demand-side management, fault detection and service restoration, voltage profile optimization, and renewable integration. All these tasks require intensive data processing. For example, real-time state estimation integrates measurements from SCADA, breaker and isolator position data (ADMS), grid topology (GIS), and possible production points (DERMS) to calculate the voltage-current condition at every point in the distribution network. If this calculation is unreliable, operators may fail to accurately identify overloaded lines or faults. Therefore, grid management applications require all data silos to be integrated and available in near real-time (The Big Data Problem For Utilities | Camus Energy) (The Big Data Problem For Utilities | Camus Energy). Electric distribution companies are increasingly recognizing the importance of granular (detailed) data; minute or second-level data analysis, instead of hourly summaries, provides an advantage in responding to rapidly developing events (Smart Grid’s Big Data And Granularity | T&D World) (Smart Grid’s Big Data And Granularity | T&D World). However, working with granular data requires effective management of big data. One challenge encountered in this area is unstructured (raw) data formats (Smart Grid’s Big Data And Granularity | T&D World). Unstructured text or image data, such as fault notes from IoT devices or maintenance teams, can also be found. Since such data does not conform to traditional database formats, integrating text mining or image analysis techniques during the ETL phase is required.

These critical areas highlight the need for high integrity, fast, and scalable ETL processes for electric distribution companies to provide competitive and reliable services. Anomalies must be scanned in real-time data streams, predictive maintenance requires processing years of multi-source data, and grid management requires integrating data from different systems into a single real-time grid picture. This requires ETL layers to address both technical (speed, volume, diversity) and managerial (quality, consistency, security) challenges.

Key Challenges in ETL Processes

Given the complexity of the data ecosystem in the electric distribution sector, various bottlenecks arise when traditional ETL approaches are applied:

Multiple and Heterogeneous Data Sources: Integrating data from countless systems that produce data in different formats and protocols is inherently difficult. ETL developers may need to pull data from a wide range of sources, from XML/CSV-based meter data to real-time MQTT streams, relational databases to cloud APIs. This often requires writing thousands of lines of code in multiple programming languages, resulting in a complex integration infrastructure (Building ETL Pipelines with AI | Informatica). Coding heterogeneous data integration manually requires expert data such large ETL workloads creates challenges regarding network bandwidth and storage write speeds. Network latency can become a significant barrier; centralizing large volumes of data can cause network traffic bottlenecks and slow down ETL processes (5 Challenges of Data Integration (ETL) and How to Fix Them | Datavail). Similarly, insufficient server resources (CPU, memory, disk I/O) can cause performance degradation in ETL processes (5 Challenges of Data Integration (ETL) and How to Fix Them | Datavail). ETL pipelines coded using traditional approaches may not scale flexibly as workloads increase, meaning they might fail to process 100% of the data quickly and fall behind. In fact, one study found that two-thirds of the data in businesses becomes older than 5 days when loaded into systems (5 Challenges of Data Integration (ETL) and How to Fix Them | Datavail). This shows that, in areas like distribution, where real-time decisions are critical, outdated data is unacceptable. The need for real-time or near real-time data integration requires solutions beyond traditional ETL designs.
Data Quality and Integrity: As discussed earlier, the lack of up-to-date, accurate, and consistent data is one of the biggest challenges for ETL. If the latest and most accurate information is not retrieved during the ETL process or if duplicate records are not filtered when combining different copies of the same data, the resulting information will be unreliable (5 Challenges of Data Integration (ETL) and How to Fix Them | Datavail). Data quality problems are common in electric distribution companies: field teams often enter non-standard expressions in fault records, meter reading errors, and noisy signals in SCADA telemetry. Therefore, data validation, outlier detection, and missing value imputation steps must be automated within the ETL workflow. Otherwise, critical applications like anomaly detection could mistakenly classify normal values as anomalies or vice versa. Another challenge to ensuring data integrity is synchronizing data from different sources. For instance, during a transformer explosion event, SCADA may record a high current instantaneously, while the OMS (Outage Management System) may start receiving customer reports five minutes later; if the ETL process doesn’t correctly combine these events on the correct timeline, the analysis system could misinterpret the cause and result. Overcoming such challenges requires the ETL process to effectively manage business rules and data relationships.
Maintenance, Changes, and Sustaining Costs: Once ETL pipelines are set up, they do not remain static; they require continuous maintenance, much like a living organism. As new data sources are added, schema changes occur in existing sources, or data volumes increase, ETL processes must be updated (5 Challenges of Data Integration (ETL) and How to Fix Them | Datavail). For example, if a new type of sensor is added to the distribution network, new code may be required to integrate it into the ETL process. In traditional approaches, this requires intervention and testing by experts, often leading to long cycles. Additionally, maintaining existing code becomes challenging as complex ETL code accumulates technical debt over time, making it brittle. As a result, many organizations need to regularly optimize and revisit their ETL processes (5 Challenges of Data Integration (ETL) and How to Fix Them | Datavail). In electric distribution companies, because of the critical nature of data, errors in ETL could have serious operational consequences, making the cost of maintenance and updates higher. Under these conditions, there is a growing trend toward self-optimizing ETL systems that require less human intervention.
The challenges outlined above highlight the limitations of traditional ETL methods in the electric distribution sector. Both the workload in data engineering and the need for speed and scale demand a shift to smarter and more automated solutions. This is where AI-assisted ETL approaches come into play.

AI-Based ETL Automation

Artificial intelligence is bringing revolutionary innovations to the field of data engineering and integration. Especially in recent years, AI-powered ETL tools have emerged, promising to complete data integration tasks that traditionally took weeks in just a few hours (Building ETL Pipelines with AI | Informatica). This section will explore three prominent approaches to automating ETL processes using AI: the use of large language models (LLMs), AutoML approaches, and reinforcement learning (RL) methods.

Automating ETL Processes with Large Language Models (LLMs)

Large language models (GPT-4, BERT, etc.) have introduced a new paradigm in data integration with their success in natural language processing. LLMs can support ETL processes in processing unstructured data and automating complex transformations (LLMs in ETL Pipelines Guide: Complete Overview & Best Practices) (LLMs in ETL Pipelines Guide: Complete Overview & Best Practices). Particularly in electric distribution, text-based data (field reports, maintenance notes, customer complaints, etc.) can be processed and structured using LLM-based ETL. For example, free text in fault reports can be automatically converted into category labels, or meaningful events can be extracted from system log entries. Some of the advantages of LLMs in ETL include:

Advanced Data Transformation: Complex transformations or difficult data merges are made easier thanks to LLM’s “understanding” capabilities. For instance, differently formatted data, such as date formats or address information, can be unified using the learned knowledge of language models. LLMs can understand uncertain or inconsistent patterns that traditional ETL tools struggle with and apply the correct transformations (LLMs in ETL Pipelines Guide: Complete Overview & Best Practices).

Natural Language ETL (No-Code Usage): The ability of LLMs to generate code revolutionizes the ETL development process. Now, a data engineer can issue a command in natural language like, “Retrieve this data from the Oracle database and load it into Snowflake, and convert the date column to ‘YYYY-MM-DD’ format,” and the LLM will understand and generate the required ETL script. With generative AI tools that emerged in 2023, models like ChatGPT can even handle tasks like writing RegEx patterns or creating integration code between different data stores without manual coding (Building ETL Pipelines with AI | Informatica). This accelerates development and allows users with limited coding knowledge to design data integration flows.

Automatic Data Cleaning and Quality Control: LLMs, trained on large datasets, can detect inconsistencies or errors in data. For instance, consider address data coming from different sources in a distribution company; LLMs might learn that variations such as “Street”, “Str.”, or “St..” all refer to the same meaning and standardize them during the ETL transformation process. Similarly, language models can assist in automatically generating data quality rules. Studies have shown that LLMs can analyze a dataset and propose potential validation rules or anomaly thresholds (Building Better Data Pipelines with Large Language Models). This enables data quality checks to be defined with less human intervention.

Concrete Examples of using LLM in ETL based Methods

Below are five concrete examples of how AI (particularly LLM-based methods) can be integrated into the ETL process in an electric distribution company:

Classifying Column Data Types and Generating Transformation Code
- Scenario: In datasets from various sources (e.g., CSV files), columns may contain numerical, date, or text data.
- Implementation:
  - The LLM automatically identifies the data type of each column by examining sample data.
  - For instance, if a “Date” column has entries like “03/15/2023” or “15-03-2023,” the LLM analyzes that column and automatically generates Python code (e.g., a convert_date function) to convert all dates to the ‘YYYY-MM-DD’ format.
  - Additionally, for an “Address” column, the LLM can detect that it contains address information and produce transformation code to split it into street, district, house number, etc.
- Benefit: Columns are processed with the correct data types and in a standard format, with no manual intervention required.
Automatic Data Cleaning and Missing Value Imputation
- Scenario: Datasets from multiple sources often contain missing, erroneous, or inconsistent values.
- Implementation:
  - The LLM inspects sample data to detect which columns have missing or invalid values.
  - Through natural language instructions (e.g., “Fill the missing numerical values in this column using the median”), the LLM chooses an appropriate imputation method and generates the transformation code to apply it.
- Benefit: Data quality is automatically improved, minimizing errors and time spent on manual cleaning tasks.
Data Merging via Fuzzy Matching
- Scenario: In different data sources like GIS (CBS), SCADA, or EAM, the same asset might be recorded under different identifiers.
- Implementation:
  - The LLM takes sample records from each dataset and defines rules for fuzzy matching based on similarity measures.
  - For example, to match the asset IDs in GIS with the corresponding records in SCADA, the LLM automatically creates a matching algorithm using similar names, addresses, or coordinate information.
- Benefit: Merging data across multiple sources is automated, ensuring data integrity by unifying records into a single complete entity.
NLP-Based Classification for Unstructured Text Data
- Scenario: Unstructured text data such as customer complaints, field reports, or fault notes may need analysis.
- Implementation:
  - The LLM analyzes the text data and automatically classifies the information it contains (e.g., “urgency,” “cause of the fault,” “location details”).
  - As a result of this classification, the data is organized into appropriate categories during the ETL process, making it ready for reporting and modeling.
- Benefit: Text data becomes structured and ready for analysis, significantly reducing manual effort in the data preprocessing stage.
Automatic ETL Pipeline Recommendations and Code Generation
- Scenario: In a data integration process, you must decide in which order and how data from different sources should be processed.
- Implementation:
  - The user gives high-level instructions in natural language, for example: “Transfer data from these sources to that target, normalize the date columns, and split address details in the text columns.”
  - The LLM interprets these instructions, outlines the necessary ETL steps, and generates Python code snippets for each step (e.g., reading data, transforming it, and loading it).
  - This way, the ETL pipeline is created and run automatically with minimal human intervention.
- Benefit: Even users with no coding experience can produce automatic ETL solutions using natural language instructions, making the process faster and less error-prone.

These concrete examples illustrate how LLM-based AI can be used to automate data cleaning, transformation, matching, classification, and pipeline recommendations across various data sets in an electric distribution company (e.g., CBS, SCADA, EAM, OSOS, SAP, TSKS, TTS). As a result, manual intervention in the ETL process decreases, data quality improves, and analysis-ready data is obtained more quickly.

Limitations

While LLM-based ETL offers several benefits, there are points to consider in its use within distribution companies. First, LLMs require high computational power and memory, which increases processing costs (LLMs in ETL Pipelines Guide: Complete Overview & Best Practices). Processing large volumes of data in real-time may not always be practical, requiring scaling using cloud-based services or specialized hardware at critical points (LLMs in ETL Pipelines Guide: Complete Overview & Best Practices). Second, integrating LLMs can be complex, requiring software integration as an API or custom module into the existing ETL infrastructure (LLMs in ETL Pipelines Guide: Complete Overview & Best Practices). Moreover, data privacy is an important concern: using a generalized model not trained with sensitive customer data can jeopardize data confidentiality. Therefore, distribution companies must implement anonymization and strict access controls when using LLMs (LLMs in ETL Pipelines Guide: Complete Overview & Best Practices). Lastly, LLM outputs may not be deterministic; they may not always produce the same result for the same input. In ETL processes, consistency is critical. Hence, it is important to validate the transformation code or results generated by LLMs and support them with fixed rules at key points if necessary (Do you use LLMs in your ETL pipelines : r/dataengineering – Reddit).

Conclusion

Overall, the LLM-based ETL approach provides great advantages, especially in processes involving unstructured data and tasks that rely on human creativity. Many steps that traditionally required heavy coding and rule writing can become faster and more flexible with LLMs. Speed is greatly improved, particularly in complex transformations, while efficiency is outstanding, especially for text-based data (LLMs are much more effective with unstructured data) (LLMs in ETL Pipelines Guide: Complete Overview & Best Practices). However, the operational cost can rise due to the increased computational resources required compared to traditional solutions (LLMs in ETL Pipelines Guide: Complete Overview & Best Practices).

References

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