Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Predict solar and wind output with high granularity and manage grid stability as renewable penetration increases.
Deploy AI forecasting models that reduce renewable integration costs by up to 40% and improve grid stability metrics by 60%.
Our systems predict solar irradiance and wind power output at 5-minute intervals with 95%+ accuracy, enabling precise grid balancing and reducing reliance on expensive peaker plants. We integrate multi-modal data from weather satellites, IoT sensors, and historical generation patterns.
We build these systems to integrate seamlessly with your existing SCADA and EMS platforms, providing a 2-4 week proof-of-concept to validate accuracy. This is a core component of our broader Energy Grid Optimization and Predictive Maintenance services, which also include Predictive Grid Asset Lifecycle Management and AI-Driven Grid Resilience Simulation.
Our AI forecasting systems translate complex data into actionable, measurable improvements for grid stability, cost efficiency, and renewable integration.
Multi-modal AI models predict solar and wind generation at 15-minute granularity with >95% accuracy for the next 48 hours, enabling precise grid balancing and reduced reliance on fossil-fuel peaker plants.
Proprietary AI models dynamically forecast grid stability metrics as renewable penetration increases, providing operators with real-time recommendations to maintain frequency and prevent blackouts.
Optimize the economic dispatch of renewable assets by predicting congestion and market conditions, directly increasing the utilization and financial return on your solar and wind investments.
Our forecasting APIs and data pipelines are engineered for zero-disruption integration with major SCADA systems and Energy Management Systems like OSIsoft PI, Siemens, and GE, delivering insights directly into operator workflows.
Machine learning identifies emerging transmission congestion and anomalous grid behavior hours before traditional threshold-based systems, allowing for preventive re-dispatch and avoiding costly penalties.
A tiered approach to developing and deploying high-accuracy, multi-modal forecasting systems for solar, wind, and grid stability.
| Capability | Proof-of-Concept | Production-Ready | Enterprise Grid Integration |
|---|---|---|---|
Forecast Granularity | Regional (1-10 km) | Substation-level (<1 km) | Asset-level (turbine/panel) |
Model Types | Single-source (e.g., wind) | Multi-modal (wind + solar) | Integrated Grid Stability AI |
Prediction Horizon | 24-48 hours | 5-7 days with uncertainty | 15-day probabilistic outlook |
Data Integration | Public weather APIs | IoT sensor + private weather | Full SCADA, market, & satellite fusion |
Deployment Time | < 4 weeks | 8-12 weeks | Custom (12+ weeks) |
Uptime SLA | Best effort | 99.5% | 99.9% with redundancy |
Support & Maintenance | Priority + 24/7 on-call | Dedicated SRE & retraining | |
Starting Price | $15K | $75K | Custom Quote |
Our forecasting systems are built on a foundation of advanced machine learning and real-time data integration, delivering the predictive accuracy and operational reliability required for high-renewable penetration grids. We focus on measurable outcomes that directly impact your bottom line and grid stability.
Our pipelines continuously monitor for data drift and forecasting performance degradation. Automated retraining triggers ensure models adapt to seasonal shifts, new asset deployments, and changing climate patterns without manual intervention, maintaining forecast accuracy above 92% year-round.
A key differentiator: we couple renewable output forecasts with physics-informed AI models that predict resulting grid inertia and stability metrics. This allows operators to proactively schedule synchronous condensers or battery storage to maintain frequency stability as renewable penetration spikes.
For critical infrastructure, we deploy forecasting models within hardware-based Trusted Execution Environments (TEEs) or fully air-gapped architectures. This ensures data sovereignty, protects against cyber-physical threats, and complies with NERC CIP and EU CSRD regulations without sacrificing model performance.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get clear answers on how we build and deploy AI forecasting systems for solar, wind, and grid stability.
Typical deployment for a production-ready AI forecasting system is 4-8 weeks, from initial data pipeline setup to model validation. This includes integrating with your SCADA/weather APIs, training initial models on historical data, and deploying to a staging environment. Complex multi-modal systems (e.g., combining satellite imagery with sensor telemetry) may extend to 12 weeks. We provide a detailed project plan within the first week of engagement.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
