Overview
Leverage state-of-the-art algorithms—powered by machine learning and optimization techniques—to maximize resource yield and enable sophisticated participation in capacity, energy, and ancillary markets.
Key Features
- Ensemble ML Models: Combine neural networks, decision trees, and clustering for robust predictions.
Real-Time Optimization: Continuously adjust dispatch strategies based on live grid and market signals.
Market Strategy Integration: Embed bidding logic for capacity, energy, and ancillary services directly into control decisions.
Reinforcement Learning: Learn from past events to improve future yield and market performance.
Automated Parameter Tuning: Self-tune algorithm settings to adapt to changing asset behavior and market conditions.
Benefits
- Maximized Asset Value: Extract the highest possible kW-hour reductions and revenue streams.
Enhanced Market Revenues: Capture new opportunities through dynamic, data-driven bidding strategies.
Operational Efficiency: Reduce manual intervention and streamline decision-making.
How it works
- Data Aggregation: Ingest telemetry, market prices, and weather feeds in real time.
Model Training: Train and validate ML models on historical and live data.
Strategy Deployment: Translate model outputs into dispatch commands and market bids.
Performance Monitoring: Track actual vs. predicted outcomes to validate algorithm accuracy.
Continuous Learning: Feed performance data back into models to refine decision logic over time.
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