Accelerate your transition to an intelligent digital energy enterprise
QR Energy DataHub Advanced Data Ecosystem
- QR DataHubis company and operation agnostic and offers robust data management capabilities within an API-friendly ecosystem.
- We enable flexible integration and data exchange with third party & in-house systems. Functionalities are constantly enhanced and extended to meet markets and clients requirements.
QR Energy DataHub
Energy, electricity load, meter quantities, prices, rates, generation, transactions, risk, settlement and billing data are often stored in multiple internal and third-party platforms, with disparate timestamps, formats and types. They can be too large in size, lack indexing for analytics, AI modeling, back office billing, and reporting.
Developing and integrating a custom enterprise data hub or warehouse is a significant investment for an energy company depending on the size and complexity of the data and the intended analytics and reporting required thereafter. Once the data hub is up and running, a sizable expert team is required to maintain the platform and provide support.
We propose an alternative approach. QR DataHub is a modern flexible multipurpose data platform with 6 integrated microservices ready out-of-the-box, automating the entire data pipeline. They can be configured on the fly to automatically create a custom data hub for any electricity and energy company.
Data Object Creation
Creation and management of entities, hierarchies of placeholder for data. For example, company, subdivisions, nested entities and their inner relationships, each leading to their own data type. These automatically create the objects that manage the DataHub.
Data Model Creation
Configure on screen, the data attributes, e.g., timescale, label, types and subtypes, etc., for each one of the data types associated with the entities previously created. This automatically creates a database table for each data type, with Api endpoint connectors.
Data Fetching
For each type of data and source, e.g., API, web-scraping, FTP, csv, multi-tab Excel, configure the sequence of data fetching, mapping, transformation and insertion to our data model. These automatically fetch and insert the data in the DataHub.
Data Governance
As data is being fetched it can undergo mathematical transformation, e.g., change of granularity, units, etc, and validation under certain rules. Data access and action are controlled by user groups. Audit Trail applies to access and changes.
Data Transform
The API service allows to perform data transformation on the fly, such as change grain data. E.g., load 1-min data from hourly data by some methods to fill the inner points. Or load hourly data from 1-min data by some method such as average, sum, min, mix etc.
API and Data Management
A flexible API service manages access and publication of all data in the DataHub.
A web dashboard allows authorized users to interact with any data in the DataHub: search, view, upload, download, edit, delete, plot and visualize.
Advanced Data Architecture
- We use Postgres Timescale DB which offers the best performance for high-frequency time series or sensor data thanks to its hypertable capabilities.
- Our data platform has an optimized architecture for big data. We use the Json and Json BLOB (binary large object) format to store multiple data in one record rather than single entry data. For example, we store weather indicators, temperature, cloudiness, UV, wind speed, etc, rather than storing these as individual data, we store them all as 1 single Json file entry with a tag for each indicator.
- The API service reads and writes individual Json tags. The Jason storage format allows much faster query and smaller storage space.
QR Data Hub for World Power Markets
Integrated Analytics
- We provide a highly advanced data analytics suite built on vectorized Python libraries, including NumPy, SciPy, and Pandas, specifically tailored to the complexities of energy financial data and electricity load and price time-series modeling and analysis.
- The platform offers a no-code analytics environment in which users construct analytical pipelines through a web-based drag-and-drop interface. Users can chain together interlinked mathematical processors to form complex operator workflows applied to time-series data. For example, a workflow may include gap-filling, outlier detection and removal, and smoothing operations such as simple moving averages (SMA), all configured without writing SQL or Python code.
- Once defined, these processing pipelines can be reused and executed automatically across multiple time series, ensuring consistency and scalability. In addition, the platform includes advanced data visualization and interactive dashboards, enabling deep exploratory analysis and insight generation as part of a unified solution.
