AI-Powered Energy Management
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Training environment: 50 NVIDIA A100 GPUs, leveraging NVIDIA’s CUDA and TensorRT frameworks to optimize both training and inference stages.
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Training time: ~1 month of continuous training with incremental fine-tuning for each new dataset integration.
Comprehensive Enrollment Insights
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Energy consumption data from 50+ utility companies over the last 5 years.
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Weather pattern data spanning 10 years, covering multiple geographic regions to predict energy fluctuations based on climate variations.
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Market dynamics data, including historical energy prices, demand-supply fluctuations, and economic activity in different regions.
Program Incentives Management
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Energy demand forecasting accuracy: ±1.5% over a 48-hour horizon.
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Real-time response latency: <50ms, ensuring immediate grid load adjustments during peak hours.
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Customer behavior prediction: Powered by deep reinforcement learning, with prediction accuracy exceeding 85% for customer energy usage patterns.
We’re continuously adding new suites, features, functionality, and integrations derived directly from utility feedback
Product Demo
