Renewable Energy Optimization

Challenge‍

A renewable energy producer was dealing with suboptimal energy trading decisions due to the inherent variability of renewable sources and market price fluctuations. Their manual trading approaches couldn't adequately account for the complexity of predicting both production capabilities and market opportunities, resulting in missed revenue potential.

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Solution

An AI energy production and market prediction system using Akkio, Claude API, and their existing energy trading platform that:

• Forecasted energy production based on weather data and equipment performance
• Predicted market pricing trends across different time windows
• Optimized energy storage versus immediate sale decisions
• Identified the most profitable trading windows for different production scenarios
• Generated trading recommendations with confidence scores
• Continuously learned from outcomes to improve future predictions

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Implementation

‍The implementation was completed in 7 weeks:

1. Production data integration and analysis to establish baseline forecasting
2. Market data integration and correlation analysis
3. Development of prediction models for both production and pricing
4. Trading strategy optimization algorithms with risk parameters
5. Decision support dashboard development for trading team

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Results

• 15% increase in trading revenue through more optimal timing of energy sales
• 8% improvement in production efficiency through better resource allocation
• 1.2 FTE saved in trading operations and analysis
• Return on investment achieved within the first 11 weeks of operation

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Risk Management

The system included configurable risk tolerance settings that allowed the company to balance potential gains against the certainty of outcomes.

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