Technical data warehouses

Main characteristics and uses of technical data warehouses

Data sources

Energy data warehouses collect data from various sources, such as:

• Electricity generation and consumption

• Gas and oil consumption

• Renewable energy sources (solar, wind, hydro)

• Performance indicators for energy networks and distribution systems

• Environmental data, such as weather information

Data collection and storage

Data can be collected using real-time sensors, smart meters and other monitoring systems. Data warehouses can usually handle large amounts of structured and unstructured data, which are stored and organised over a long period of time.

The collection of data in time series is particularly important in energy data warehouses, as tracking changes in energy consumption, production and other parameters over time is critical for system optimisation and analysis.

Analysis and processing

Data warehouses provide the possibility to analyse data in detail. Different methods and tools are used to analyse the data, such as:

• Data mining

• Statistical analysis

• Machine learning and artificial intelligence-based models

• Machine learning and artificial intelligence techniques

Technical characteristics

Large data handling:

• Storage capacity: multi-terabyte or petabyte capacity to be able to store huge amounts of data.

• Data compression: effective data compression techniques to optimise storage space and data transfer speed.

Data processing capabilities:

• Real-time data collection: Frequent refresh cycles (e.g., per-second, per-minute data) for real-time monitoring and fast responsiveness.

• Batch data collection: Periodic data collection (e.g. hourly, daily) for longer-term trends and analysis.

• Data gaps and errors (outliers) management: Data gaps and errors, in particular outliers, are a key part of the data processing process. The aim is to ensure that data quality problems do not interfere with query and analysis tools.

Data architecture:

• Time-series databases: databases specifically designed to handle time-series data

• Scalability: Horizontal and vertical scalability as the amount of data increases.

  • Horizontal scalability: Ability to increase capacity by adding additional servers or nodes.

  • Vertical scalability: Increase performance by using more powerful hardware.

Timestamping:

• Accuracy: Provide high precision timestamps, accurate down to nanoseconds, for precise time tracking.

• Synchronization: Time synchronization between different data sources (e.g. using Network Time Protocol - NTP).

Data compression and storage:

Data compression techniques: effective data compression to optimise storage of time-series data (e.g. delta encoding, gorilla compression).

• Storage layer: In data warehouses, data is organized into different layers based on different access speeds and storage requirements:

• Real-time storage (hub): a fast-access, low-latency database that provides immediate access to data.

• ODS: The storage of data in its original format, necessary for data quality control and long-term processing.

• Data warehouse layer: Structured, integrated and consistent data storage that supports historical analysis and business decision making.

• Analysis layer (OLAP cube): Optimised data storage for decision support and analysis, enabling fast queries and complex analyses.

Data integrity and security:

• Data replication: redundant data storage to avoid data loss.

• Data security: encryption and access control to protect sensitive data.

Data visualization and analysis

• Visualisation tools: interactive graphs that can be customised to show data in real time for easy understanding. Dashboards for real-time aggregation and visualization of multiple data sources.

• Analytical tools: statistical analyses that help you examine data using different statistical methods. Machine learning algorithms to identify predictive models and patterns.

• Online data analysis: real-time data processing and visualisation through continuously updated graphs for instant decision-making.

• Static reports: pre-prepared reports that contain fixed data but can be interactively explored, for example, by drilling down and aggregating.

Structure of the architecture

Data source layer:

• Sensors and Meters: real-time data collection from different energy systems.

• SCADA systems: Supervisory Control and Data Acquisition systems that integrate data from different sources.

• External data sources: weather data, market data, etc.

Data acquisition and integration layer:

• Data Acquisition Systems: Capable of collecting data continuously and intermittently.

• Gateway and data collectors: Intermediate devices that collect and pre-process data.

• ETL (Extract, Transform, Load) processes: Extract, transform and load data into the data warehouse.

• IoT platforms: Internet of Things platforms that manage sensor networks and data transmission.

• Monitoring and alerting: monitoring the status of data collection devices and raising alerts when abnormal events occur

Data processing layer:

• Stream processing: instant processing and analytics of real-time data (e.g. Apache Kafka Streams, Apache Flink).

• Batch processing: periodic processing of large amounts of data, allowing long-term trend analysis and complex analytics (e.g. Apache Spark, Hadoop).

• Machine learning fitting: Machine learning consists of two main components. One is the offline learning phase, where models are trained on historical data, optimising predictive accuracy. The other is the online evaluation, which is able to perform predictions in real-time or near real-time, even on continuous data streams (datastream), thus providing immediate decision support.

• Data mining tools: Statistical and predictive models to identify hidden relationships, patterns and anomalies in data.

Data warehouse layer:

• Data storage systems: high-performance and scalable databases (e.g. SQL, NoSQL).

• Metadata management: Metadata management for data.

• Time-series databases: Specialised databases optimised to handle time-series data.

• Data compression and storage techniques: Specific data compression and scalable storage solutions.

Data user layer:

• Dashboards and reporting: User-friendly interfaces for data visualization and reporting.

• Data access APIs: providing APIs for developers and other systems to access data.

Security and administration layer:

• Access control: managing user privileges.

• Data protection and security measures: encryption, audit logs, intrusion detection systems.

Areas of use

• Energy data warehouses can be used in many areas, including:

• Monitoring energy systems

• Energy systems optimization

• Energy production forecasting

• Energy consumption forecasting

  • Forecasting faults

  • Anomaly detection

• Monitoring the load and usage of network elements

• Meeting sustainability targets and reducing environmental impact

• Improving the reliability of energy supply

• Supporting network development 

Benefits

• Increased efficiency: energy processes and systems can be optimised to reduce energy losses and increase efficiency.

• Cost reduction: better forecasting and optimised operation can reduce operating costs.

• Sustainability: Data can be used to better monitor and reduce environmental impacts, contributing to more sustainable energy management.

Summary

Energy technical data warehouses are complex systems composed of many technological and architectural elements. These systems ensure efficient and secure data collection, storage and analysis, which is essential for the modernisation and optimisation of the energy industry.

Time-series data collection in energy data warehouses is critical for system optimisation and decision making.

Providing the right technical features and architecture enables accurate, real-time monitoring and analysis of data, which is essential for efficient energy use and sustainable operations. Time-series databases, real-time and batch processing engines, and advanced visualisation tools all contribute to increasing system performance and reliability, while enabling decision makers to better understand and optimise energy systems.