Predictive Analytics for Renewable Energy: 12% Grid Balancing Reduction

Team Composition:

  • ML Engineer
  • Backend Developer
  • DevOps Engineer
  • QA Specialist
Green Tech

We developed a predictive analytics module that achieved up to 89% forecast accuracy and reduced manual grid balancing by 12%, helping the client optimize renewable energy distribution across multiple European markets.

  • Python
  • FastAPI
  • AWS
  • Docker
  • PyTorch
  • Prophet
  • Airflow
  • PostgreSQL

Background

Our client is a European renewable energy company providing digital tools for smart grid management and solar production. Their platform connects households, solar installations, and storage systems to monitor energy flows in real time and empower users to manage their power more efficiently.

With growing adoption across multiple European markets, the company sought to go beyond reactive monitoring and introduce AI-powered forecasting. The goal was to predict both consumption and production trends, helping energy providers reduce balancing costs and plan grid capacity more sustainably.

They approached us with the request to design and deliver a predictive analytics module.

CHALLENGE

The platform already offered comprehensive real-time analytics and historical reporting. However, the client wanted to extend its intelligence into predictive operations to optimize system efficiency and renewable usage.

The main challenges included:

  • Forecast reliability: Ensuring accurate 24–48-hour predictions for renewable energy generation and demand across diverse user profiles.

  • Data diversity: Combining IoT sensor data, weather feeds, and grid load metrics into a unified, high-quality dataset.

  • Integration & performance: Deploying predictive models within an established AWS infrastructure without disrupting live operations.

  • Specific quality metrics: forecasting results should be measured in 2 dimensions: hourly and daily metrics.

  • Security & compliance: Maintaining GDPR alignment and strong data protection across multiple European regions.

Solution: Time Series ML Forecasting System

To address the project goals, Beetroot assembled an ML development team with expertise in data engineering, model development, and secure deployment. Key contributions included:

  • Data Engineering & ML Pipelines:

    Built ETL workflows using Apache Airflow and AWS S3 to consolidate IoT, weather, and grid operations data into a unified schema ready for time series analysis.

  • Predictive Model Development:

    Trained short-term forecasting models based on gradient boosting and LSTM architectures. The hybrid approach achieved strong accuracy in early tests, comparable to published benchmarks in European grid forecasting (Mean Absolute Percentage Error (MAPE) ≈ 1.5–2%).

  • Model Evaluation & Validation:

    Established validation workflows using backtesting and rolling-window evaluation to assess forecast stability across different time horizons and operating conditions, ensuring models performed reliably before and after deployment.

  • Integration & Delivery:

    Deployed a FastAPI-based microservice providing RESTful access to predictions for energy dashboards and partner APIs.

  • Secure AI Framework:

    Deployed within Dockerized AWS ECS environments using Beetroot’s Secure AI Framework — encrypting data, enforcing access control, and enabling auditability.

  • Monitoring & Maintenance:

    Integrated Grafana dashboards for continuous performance tracking and automated retraining pipelines to manage seasonal or regional data drift.

Beetroot’s team helped us take the next step in our energy management strategy. The predictive analytics module they built has already delivered promising results, and the insights keep improving as the models learn. The team shows great ownership and care for the work they do, they are not afraid to challenge our methods and propose better solutions.

Head of Data & Analytics,

Renewable Energy Company

Results

After 12 months in production, the predictive analytics solution delivered consistent operational improvements and validated its business value:

  • Stable performance under live production conditions, with forecasting and decision-support workflows running continuously without service interruptions

  • 85–89% accuracy

    for 24-hour demand and solar generation predictions

  • 10–12% reduction

    in manual grid balancing interventions

  • 8% decrease

    in reserve energy purchasing costs

  • 9% higher share

    of local renewable use during peak hours

  • 100% GDPR

    compliance verified through internal and third-party reviews

Facing a similar challenge?

Let’s talk about how Beetroot can help you design and implement scalable GreenTech solutions that improve forecasting accuracy, operational efficiency, and long-term resilience.