WindIO: International Data Standard for Wind Energy
Leading the development of the IEA Wind-adopted ontology that enables interoperability across the global wind energy modeling ecosystem
The Challenge: Fragmented Tools, Incompatible Data
The wind energy research community relies on dozens of specialized simulation tools—from engineering wake models to high-fidelity CFD codes. Before WindIO, each tool used its own data format, making it nearly impossible to compare results, validate models across fidelities, or share standardized test cases. This fragmentation was a major bottleneck for research collaboration and industrial adoption.
The Solution: A Machine-Readable Ontology
I led the development of WindIO, a JSON schema and Python interpreter that provides a common language for wind energy system data. WindIO enables any compliant tool to read the same input files and produce comparable outputs. This is not just a file format—it's a complete ontology that captures the semantics of wind turbines, wind farms, atmospheric conditions, and electrical systems.
Integrated Platforms
Impact: International Reference Systems
WindIO has been adopted by IEA Wind Task 37 and Task 55 as the official format for defining reference wind plants. These reference systems serve as benchmarks for the entire global research community, enabling reproducible comparisons of optimization algorithms, control strategies, and flow models.
IEA Wind 740-10-MW ROWP
Released March 2024
- 74 × IEA 10-MW turbines
- Based on Borssele III/IV site
- Regular and irregular layouts
- Complete collection system design
IEA Wind 2200-22-MW ROWP
In Preparation
- 100 × IEA 22-MW turbines
- Hollandse Kust West site
- Three interconnected sub-sites
- Neighboring wake effects considered
WIFA: The Operational API
Building on WindIO, I developed major contributions to WIFA (Wind Farm API)—an operational interface that allows researchers to run the same wind farm case across multiple fidelity levels with a single function call. WIFA enables systematic verification and validation (V&V) studies that were previously impractical due to data incompatibility.
WIFA Capabilities
- Unified interface to engineering models (PyWake, FOXES), atmospheric perturbation models (WAYVE), and CFD (Code_Saturne)
- Supports varying input types: hub-height conditions, vertical profiles, full 3D flow fields
- Enables multi-fidelity uncertainty quantification studies
- Powers the EU FLOW project's validation benchmarks
- Real-world validation cases including AWAKEN field campaign and open-source SCADA datasets
WIFA-UQ: Uncertainty Quantification for Wake Models
Wake models used in wind farm design carry inherent uncertainty from simplified physics and uncertain input parameters. I developed WIFA-UQ, a framework that systematically calibrates wake models against high-fidelity references (LES, SCADA) and quantifies residual bias using machine learning. The result: actionable uncertainty bounds on power predictions, plus physical insight into when and why models fail.
Three-Stage Pipeline
1. Calibration
Global calibration finds a single optimal parameter set across all conditions. Local calibration predicts condition-dependent parameters using ML, learning that e.g. wake expansion should increase under stable stratification.
2. Bias Prediction
Residual model error is predicted as a function of atmospheric features (ABL height, wind veer, turbulence intensity) using XGBoost, Polynomial Chaos Expansion, or Sliced Inverse Regression.
3. Physics Insights
SHAP analysis, partial dependence, and error regime clustering reveal why models fail: e.g., systematic underestimation under stable stratification with high wind veer.
Key Features
- Config-driven YAML workflow: preprocessing, database generation, calibration, prediction, and insights in a single pipeline
- Multi-farm cross-validation with Leave-One-Group-Out splitting for inter-site generalization
- Full WindIO/WIFA integration: any WIFA-supported wake model can be calibrated and corrected
- Sensitivity analysis via SHAP values, Sobol indices (PCE), and SIR projection directions
Technical Contributions
- Schema Design: Defined JSON schemas for turbines, layouts, atmospheric resources, electrical systems, and bathymetry
- Python Interpreter: Built validation and parsing tools that ensure schema compliance
- FAIR Principles: Designed for Findable, Accessible, Interoperable, and Reusable data
- V&V Framework: Enabled systematic model verification and validation across fidelities
- Reference Plant Authorship: Co-authored the IEA Wind 740-10-MW and 2200-22-MW reference plant specifications
Publications & Resources
WIFA API Paper
Journal of Physics: Conference Series, 2024
WCOMP V&V Paper
Wake model verification using WindIO
EU FLOW Data Hub
WindIO-formatted validation datasets
Knowledge Engineering for Wind Energy
Wind Energy Science, 2024
Future Development
WindIO continues to evolve under IEA Wind Task 55. Upcoming work includes extended resource definitions, turbine response data, and the release of additional reference plants (both land-based and offshore). The goal is a complete digital twin framework for the wind energy industry.