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Renewable Energy Optimization

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Case Study

Driving Better Decisions Through Data

This case study highlights how Amesium Analytics transforms raw data into meaningful insights that support smarter business decisions. By analysing challenges, implementing tailored data solutions, and delivering clear outcomes, we help organisations improve efficiency, strengthen operations, and achieve measurable growth.

Renewable Energy Optimization

Delivering predictive analytics and forecasting models to improve energy production and operational planning.

Client

  • Renewable Energy Provider

Duration

  • 10 Weeks

Team Package

  • Premium Package (4-Person Team)

INTRODUCTION

A renewable energy provider operating wind, solar, and battery storage assets needed better forecasting tools to optimise production, reduce operational costs, and improve demand prediction. We built an Azure-based predictive analytics solution tailored for real-time planning.

THE CHALLENGE

The provider faced complexity across multiple energy sources:

  • Unpredictable output variations (weather-based)
  • Manual forecasting models
  • Limited visibility of asset performance
  • No unified data platform across wind, solar, and battery units
  • High dependency on Excel and manual reporting

They required an automated, scalable, predictive analytics solution.

THE SOLUTION

We developed a predictive analytics platform using Azure Databricks and Power BI to unify energy datasets and deliver accurate forecasts.

  • Unified data lake for historical and real-time energy data
  • ML-based predictive models for production forecasting
  • Integration of weather forecasts and pricing data
  • Automated model training and monitoring
  • Power BI dashboards for operational insights

This enabled precise production planning and pricing strategy adjustments.

OUR APPROACH (4 Steps)

1. Data Discovery & Integration

Collected and integrated data from wind turbines, solar arrays, battery systems, and external weather sources.

2. ML Model Development

Built predictive models in Azure Databricks for production and demand forecasting.

3. Analytics & Reporting Layer

Developed dashboards for production trends, forecast accuracy, and asset performance.

4. Deployment, Training & Handover

Implemented automated retraining pipelines and provided training for operational teams.

OUT RESULTS

The energy provider saw measurable improvements:

  • Up to 30% improvement in forecast accuracy

  • Improved energy trading strategy with real-time pricing insights

  • Reduced manual forecasting workload

  • Optimised asset usage across all energy units

  • Enhanced visibility for planning and operations teams

TECHNOLOGIES USED

    • Azure Databricks

    • Azure Machine Learning

    • Azure Data Lake

    • Power BI

    • Azure Data Factory

    • Weather/External Data Integration APIs

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