Bring Renewable Energy
to CrewAI

DNV Renewables provides over 40 climate datasets including: Mesoscale wind data (global and regional), Solar irradiance (GHI, DNI, DHI), Reanalysis datasets (ERA5, MERRA-2), Global wind energy atlas, Temperature, pressure, humidity, and more. Data covers onshore and offshore locations worldwide with time series from 1980s to present.

Visit the DNV Renewables platform and contact your DNV account manager or visit the EMD/DNV Renewables website to request API access. Once your account is provisioned, you'll receive an API access token from your account dashboard. This token authenticates your requests and is linked to your subscription plan.

The data ordering process is: 1) Check availability for your location, 2) Place an order with dataset, coordinates, and time period, 3) Wait ~30 seconds for processing, 4) Check order status until 'success', 5) Download the generated time series file. Note: Download links expire after 12 hours, so download promptly. Rate limit is 10 orders per 10 minutes.

Mesoscale climate data comes from numerical weather prediction models that simulate atmospheric conditions at regional scales (typically 1-50 km resolution). Unlike single-point measurements, mesoscale data provides spatially consistent, long-term time series essential for renewable energy resource assessment. It's used for wind farm siting, solar project planning, and long-term energy yield analysis because it captures multi-decadal climate patterns that short-term measurements miss.

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.