Multi-Agent Based Automation of Solar Farm Site Selection and Performance Monitoring

Manual analysis of land parcels for solar suitability is a bottleneck, requiring weeks of GIS work and consultant reviews. A custom multi-agent workflow automates the ingestion and scoring of 10+ criteria—including solar irradiance (from NASA POWER), land slope (from LiDAR), proximity to grid interconnects, environmental constraints, and land ownership data—to rank thousands of parcels in hours. This compresses the pre-acquisition phase, allowing developers to secure options on high-yield sites faster than competitors.

Suboptimal turbine or panel placement within a site can erode project IRR by 5-15% due to shading, inefficient cabling, or terrain challenges. An autonomous layout agent uses digital terrain models and shading simulations to generate multiple array configurations, balancing energy yield against earthwork and balance-of-system costs. By integrating with CAD tools (e.g., AutoCAD, PVsyst), it produces construction-ready plans that maximize kWac per acre, directly improving the project's capital efficiency.

Post-construction, soiling (dust, pollen) and vegetation encroachment can reduce annual energy production by 3-10% if not addressed. A monitoring agent schedules and analyzes frequent satellite (e.g., Planet) or drone imagery, using computer vision to detect panel soiling levels and classify vegetation growth within the site perimeter. It automatically generates prioritized cleaning and trimming work orders in the CMMS (e.g., SAP EAM), turning reactive maintenance into a yield-protection system.

Lenders and investors demand high-confidence yield forecasts and operational transparency. This workflow creates an auditable data trail from initial site scoring through to operational performance, feeding directly into asset management platforms (e.g., Power Factors, AlsoEnergy). Automated reporting on key performance indicators (KPIs) and deviation alerts provides the evidence needed for favorable financing terms and supports higher secondary market valuations by demonstrating proactive, data-driven operations.

Managing a distributed fleet of solar assets creates operational overhead and visibility gaps. The workflow centralizes intelligence, where a supervisory orchestration layer (built with LangGraph) coordinates the site-selection, layout, and monitoring agents across the portfolio. It integrates with the corporate GIS, ERP for CAPEX tracking, and SCADA for live telemetry, giving asset managers a single pane of glass to prioritize capital recycling and operational spend across hundreds of sites.

A production implementation follows a phased approach: a 3-week pilot on a single development site validates the data pipelines and agent logic. Weeks 4-8 involve hardening the integration with enterprise systems (Salesforce for CRM, SAP for project costing, DroneDeploy for imagery). The final phase rolls out the monitoring agents across an operational fleet, with built-in approval gates for layout changes and exception routing for ambiguous imagery. The architecture is cloud-agnostic, typically deployed on AWS/Azure with Kubernetes for agent scalability.

This agent ingests and fuses multi-source geospatial data—solar irradiance maps (from NASA/PVGIS), land slope (from SRTM/LiDAR), proximity to grid infrastructure, and environmental constraints (wetlands, protected areas). It applies weighted multi-criteria analysis to rank thousands of potential parcels, outputting a shortlist of viable sites with quantified scores. The agent integrates with ArcGIS or QGIS for visualization and exports results to development pipeline tools.

Triggered by the suitability shortlist, this agent pulls local energy pricing, estimated construction costs, and interconnection fee schedules. It runs Monte Carlo simulations on energy yield (using PVsyst-compatible models) and financial returns, factoring in incentives and degradation. Outputs are comparative pro formas that feed directly into investment committee dashboards or Salesforce CRM for deal tracking.

Once operational, this agent continuously ingests Sentinel-2 or Planet satellite imagery to monitor panel soiling (via spectral reflectance analysis) and vegetation encroachment (via NDVI time-series). It correlates findings with SCADA performance data to pinpoint underperforming strings or inverters, automatically generating work orders in IBM Maximo or SAP EAM for cleaning or trimming crews.

Built on LangGraph or Temporal, this layer manages the stateful workflow, passing context between agents, handling approval gates (e.g., environmental review), and routing exceptions to human planners via Slack or Microsoft Teams. It maintains a full audit trail of all decisions and data sources for compliance and provides observability dashboards for pipeline health.

A resilient, containerized pipeline (using Apache Airflow or Prefect) orchestrates the ingestion, preprocessing, and storage of raster and vector data from sources like USGS, ESA Copernicus, and commercial providers. It normalizes data into a unified spatial format and serves it via APIs to the agent network. The hub also handles bidirectional syncs with external systems like Autodesk BIM for design and GE Vernova's M&D for asset management.

Deployment involves a phased pilot on a single region, with agents initially in 'recommendation-only' mode to validate outputs against expert judgment. Critical architecture components include a human-in-the-loop review queue for high-value sites, model performance monitoring for drift in geospatial classifiers, and robust data governance to ensure the use of licensed and current satellite imagery.