Solutions de surveillance et de maintenance de la production d'énergie : 20% moins d'énergie perdue

Innowise has developed monitoring solutions for managing wind and solar farms, preventing loss risks, and managing turbine construction.

Client

Industrie

Durabilité

Région

Client depuis

2022

Our client, a leading global renewable energy provider, operates across wind, solar, hydropower, and biomass sectors. While they continue to manage conventional power plants (coal, gas, and nuclear), their focus is on achieving carbon neutrality by 2040.

Les informations détaillées sur le client ne peuvent être divulguées en vertu des dispositions de la NDA.

Défi

Tackling inefficiencies in wind and solar farm operations

Our client faced significant challenges in efficiently managing multiple facets of their wind and solar farms, reporting issues with energy data management, and construction monitoring. The primary pain point was the lack of an integrated system that could handle the diverse and complex data from various sources. This resulted in operational inefficiencies and ballooning costs. Additionally, the client needed a solution for compliance with REMIT standards for transparency market reporting. To solve these issues, they required a scalable, data-driven system that would integrate all aspects of their operations and enhance decision-making.

Solution

Transforming renewable asset management with smart solutions

Innowise team embarked on a complex project encompassing several streams to improve wind turbines and solar panels monitoring and maintenance, while also ensuring full regulatory compliance.

Turbine maintenance API

Our project team developed the API interface with two main components: backend services for data synchronization and an interface for querying turbine data. Technically, the backend service pulls data from the central system, including SAP sources and other databases. This data is stored in a centralized database to support complex queries and data integrity. This process involves regularly scheduled tasks synchronizing wind turbines and solar panels’ state data and maintenance schedules. We utilized batch processing to handle data alignment, so that real-time updates are reflected efficiently.

The API returns data in JSON format, allowing for easy integration with existing systems and straightforward parsing by client applications. The API interface provides a flexible and efficient way for developers to query specific data about internal projects and maintenance schedules and provide a robust, efficient, and scalable system for managing wind turbine data.

Wind farm monitoring app

To further optimize energy operations, we built a web application for wind farm monitoring that tracks turbine performance, wind speed, and electricity generation. We started with the integration layer that aggregates data from different turbines using unique event types and communication protocols. This layer normalizes data into a unified format for consistent processing and analysis.

The application’s core includes advanced algorithms for turbine dispatching that analyze wind speed, air temperatures, pitch angle, electrical parameters, and rotor speed. The yaw system is evaluated separately to maintain predictable and controlled performance.

Additionally, users could set parameters for predictive maintenance, such as reducing operational capacity during high wind forecasts to prevent damage.

Energy-related data migration

Recognizing the need for a cost-efficient and scalable data solution, we migrated the client’s energy-related data (energy prices, production volumes, profit margins, etc.) from an expensive Oracle database to a streamlined Dremio data warehouse. Designed for analytical purposes, Dremio supports multiple data sources and provides tools for data curation and querying.

We developed custom scripts using Python and SQL that apply transformation operators to the data throughout the migration. These operators handle data type conversions, merge records, clean data, and reformat dates to match the Dremio schema requirements. Also, our specialists implement predefined rules that analysts can apply to data sets, making it easier for end users to manage data transformations without deep technical knowledge.

Furthermore, we created a suite of unit tests to validate each script’s and transformation operator’s functionality, ensuring optimal performance. These tests cover various scenarios to guarantee the data’s accuracy and completeness under various conditions.

Energy loss monitoring

To prevent energy production losses, we designed and deployed a real-time monitoring tool that aggregates data from turbine sensors, weather stations, and solar panels. This data comprises real-time readings on wind speed, sunlight exposure, air temperature, and mechanical status of the energy-producing equipment. Advanced algorithms continuously analyze the incoming data to identify deviations from expected performance benchmarks. These anomalies indicate possible losses, such as a drop in turbine efficiency caused by misalignment, wear, or suboptimal solar panel angles. Based on these data, customers’ technicians make informed decisions on preventing energy production losses.

Construction monitoring

In addition to monitoring and maintenance, we forged a web application that streamline the tracking of wind turbine and solar panel construction projects. It covers everything from project planning and budgeting to operational unit maintenance. The solution serves as a centralized repository for managing project timelines, involved parties, budget details, and regular checkup records, ensuring maintenance activities are logged and tracked against scheduled intervals. This comprehensive solution enabled the customer’s stakeholders to compare real-time construction progress against planned milestones, highlight discrepancies for corrective action, and make timely adjustments to keep projects on schedule and within budget.

Commercial transparency and market reporting

Staying compliant with energy market regulations requires accurate reporting and strict control over sensitive data. To support our client, we automated key parts of their transparency and commercial reporting process.

The system starts by integrating unavailability data into energy production calculations. This step provides a reliable foundation for everything that follows, helping the client base their operational and financial planning on real-time conditions.

Then, we addressed data confidentiality under REMIT regulations, which require companies to report production data before using it for commercial purposes. To meet this requirement, our team built a secure data flow that locks access to sensitive data until it’s officially disclosed. This protects the client from the risk of premature use and potential violations.

Next, we connected the system to the EEX platform to handle REMIT reporting automatically. While EEX functions as an independent entity, this integration helps the client meet transparency obligations without manual effort.

Only after regulatory reporting is complete the system proceeds to commercial tasks such as nomination and trading. With secure data handling and automated workflows, the client now operates with full compliance while improving efficiency and reducing risk.

Technologies

Frontend

React, Typescript, Redux, Javascript, HTML, CSS

Backend

.NET, C#, Node.js, NestJs, Python, Entity Framework, LINQ, Quartz.NET, Hangfire, ASP.NET Core, Web API, REST API, RabbitMQ, GraphQl, .Net Framework, .NET 3.1, .NET 6, .NET 8 Swagger, EasyNetQ, PostgreSQL.f

Base de données

PostgreSQL, MySQL, Dremio, Oracle, Redis

Cloud & data platforms

Azure (App Services, KeyVaults, Functions, Databricks, etc.), AWS

Surveillance du serveur

Grafana, Prometheus, Rancher

Intégrations

GraphQL, REST APIs, RabbitMQ, Kafka

DevOps

Kubernetes, Docker, Docker Compose, Terraform, Helm, Azure DevOps

Systèmes de contrôle de la révision

Git

Gestion des logs

Serilog, OpenTelemetry, Jaeger

QA

PyTest, Xunit, AutoFixture, Moq

Project Management & Collaboration

Atlassian Jira, Atlassian Confluence.

Équipe

Développeurs Back-End

Développeurs Front-End

Responsable technique

Responsable de produit

Chef de projet

Ingénieurs QA

Ingénieurs DevOps

Ingénieurs en données

Résultats

20% less energy losses and 2x more accurate construction timelines

Innowise upgraded the client’s wind turbine and solar panel monitoring and maintenance systems, significantly improving efficiency and reducing operational costs. The system now provides real-time monitoring and data analysis, enabling precise maintenance scheduling that minimizes downtimes and extends equipment lifespans. As a result, operational efficiency has increased, leading to higher energy output and lower costs associated with emergency repairs and unscheduled maintenance.

By continuously analyzing performance data and detecting energy losses, the app ensures that wind and solar farms operate at peak capacity, maximizing energy production.

Additionally, the web application for tracking wind turbine and solar panel construction gives the client real-time visibility into project timelines and budgets. This helps identify potential delays and budget overruns early, allowing for immediate corrective action. Automated commercial reporting improves accuracy and eliminates manual errors, while strict data access controls safeguard sensitive information from premature disclosure.

Durée du projet

Décembre 2022 - En cours

20%

reduction in energy losses

more accurate turbine construction timelines

Exemples de projets similaires

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Solutions de surveillance et de maintenance de la production d'énergie : 20% moins d'énergie perdue

Client

Défi

Tackling inefficiencies in wind and solar farm operations

Solution