AI Agentic Workflow for Hyper-Local Solar Irradiance and Power Forecasting

Inaccurate solar forecasting creates direct operational costs through suboptimal trading, unnecessary curtailment, and missed demand response opportunities. A 5% forecast error can erode 10-15% of a solar asset's annual revenue from wholesale markets and grid services. This workflow automates the ingestion of satellite imagery, sky camera feeds, and on-site sensor telemetry to produce hyper-local, probabilistic forecasts that drive higher-confidence bids into day-ahead and real-time energy markets.

Implementation integrates with energy management systems (OSIsoft PI, Siemens Spectrum) and market platforms via APIs. The architecture includes automated model retraining triggers based on performance drift and an approval gate for forecasts exceeding confidence thresholds before dispatch. This creates a closed-loop, production-grade system that reduces revenue leakage and improves asset utilization without requiring constant manual oversight from operators.

The operational bottleneck is the manual stitching of satellite feeds, sky camera imagery, and on-site sensor telemetry into a single, reliable forecast. This workflow automates that fusion, reducing forecast error by 20-40% and directly improving revenue capture for solar asset owners by enabling more precise bidding into day-ahead and real-time energy markets. The architecture ingests data via dedicated agents, each responsible for a specific modality and its quality control, feeding a central orchestrator.

Implementation integrates with platforms like OSIsoft PI for sensor data and leverages LangGraph for agent orchestration. The forecast API feeds directly into systems like Siemens PSS®E for grid analysis or custom Python-based trading engines. Critical controls include automated model retraining triggers based on performance drift, a human review queue for low-confidence predictions, and comprehensive observability dashboards tracking mean absolute percentage error (MAPE) against settlement data to ensure continuous ROI validation.

The operational bottleneck is the manual, error-prone process of synthesizing disparate data sources into a reliable, site-specific power forecast. This workflow automates that synthesis, delivering a 15-30% improvement in forecast accuracy. The resulting reduction in forecast error directly translates to higher revenue from energy and ancillary service markets, lower imbalance penalties, and more efficient battery dispatch, protecting asset economics in volatile microgrid environments.

Implementation begins with a pilot phase integrating a single data source and a baseline model into a sandboxed energy management system (EMS). Phase two adds multi-source fusion, LangGraph orchestration for agentic data validation, and integration with primary SCADA. The final production phase deploys automated model retraining, uncertainty-based alerting to human operators, and full integration with trading platforms (e.g., custom interfaces to ISO markets) and DERMS for closed-loop control, governed by change-management protocols.

Deploying this forecasting workflow requires a phased rollout with strict governance to protect grid reliability and asset revenue. Begin with a single-site pilot, integrating the ML pipeline's outputs—power forecasts with uncertainty bounds—into a sandboxed version of your Energy Management System (EMS) or SCADA. Establish a human-in-the-loop approval gate for all automated dispatch decisions during this phase, using the discrepancy between forecasted and actual generation to calibrate model confidence and trigger automated retraining protocols. This controlled approach validates data quality from satellite, sky camera, and on-site sensors before scaling.

Full-scale deployment automates the approval gate for validated sites, but maintains continuous monitoring for forecast drift and exception routing. The architecture must embed observability: every forecast and subsequent trading action is logged with its rationale, model version, and input data signatures for audit. Implement circuit breakers that can revert to conservative, rule-based dispatch if prediction error exceeds a defined threshold or if data feeds are interrupted. This control plane, built alongside the core ML pipeline in LangGraph or a custom orchestrator, ensures the workflow's operational integrity and provides the defensible audit trail required for participation in regulated energy markets.