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Optimizing Grid Reliability with AI-Powered Load & DER Forecasting
Challenge – DER Growth Driving Feeder Overloads
A regional utility experienced rising operational stress across 20 feeders as distributed energy resource (DER) adoption accelerated.
Frequent feeder overloads and limited hosting capacity constrained renewable integration, while inefficient demand response (DR) targeting reduced flexibility and increased curtailment costs.
Traditional load forecasting methods failed to account for dynamic DER and weather interactions, limiting predictive accuracy and proactive planning.
Solution – AI-Driven Load and DER Forecasting with Nova
Partnering with Nuodata, the utility deployed a Load & DER Forecasting solution leveraging Nova (Nuodata) and the Load & DER Forecasting Blueprints to enhance situational awareness and operational efficiency.
The solution included:
Feeder and substation-level forecasts integrating weather, DER, and consumption signals
Hosting capacity simulation to assess renewable integration limits
Demand Response (DR) targeting models to identify optimal participants and timings
Automated analytics workflows enabling near real-time operational decisions
Built on a scalable AI architecture, the system empowered grid operators to anticipate overloads, optimize DER utilization, and dynamically manage demand response programs.
Impact – Reliable Forecasting and Lower Operational Costs
The initiative delivered tangible performance improvements across grid operations:
18% reduction in overload events across high-stress feeders
11-point improvement in DR effectiveness, enhancing flexibility and participation
Lower curtailment costs through proactive load balancing and DER optimization
Improved reliability, grid visibility, and planning agility
By combining AI forecasting with DER-aware intelligence, the utility achieved a more resilient, efficient, and future-ready grid.
Meta Description
Learn how a utility leveraged Nuodata’s Nova platform to deploy AI-driven load and DER forecasting—reducing feeder overloads by 18%, improving DR effectiveness by 11 points, and lowering curtailment costs through predictive grid intelligence.
