📦 Resource excel

IEC 61400-25-4 Wind Forecast Interface Compliance Template

The IEC 61400-25-4 Wind Forecast Interface Compliance Template is an Excel-based resource that implements the data modeling, communication protocols, and conformance requirements specified in IEC 61400-25-4 for exchanging wind power forecast information between wind farms, grid operators, and forecasting systems. It ensures interoperability by mapping forecast data (e.g., time series, uncertainty metrics, metadata) to standardized logical nodes, data attributes, and Common Data Classes (CDCs) defined in the standard. Designed for AI-augmented renewable forecasting infrastructure, it supports automated validation, schema alignment, and integration with IEC 61850-based SCADA and EMS platforms.

📖 Overview

IEC 61400-25-4 is part of the IEC 61400-25 series specifying communication standards for wind turbine monitoring and control systems, with Part 4 specifically addressing forecast data exchange using the IEC 61850 information modeling framework. The compliance template operationalizes this standard by providing a structured Excel workbook containing validated templates for forecast models—including deterministic and probabilistic forecasts—alongside mandatory metadata (e.g., forecast horizon, update frequency, spatial aggregation level, confidence intervals), quality indicators (e.g., MAE, RMSE, Brier score), and semantic mappings to IEC 61850 logical nodes such as 'WINDF' (Wind Forecast) and 'WINDP' (Wind Power Forecast). The template enforces strict adherence to the standard’s object-oriented model: each forecast dataset is represented as a Logical Node instance with standardized Data Objects (DOs) like 'tstmp' (timestamp), 'val' (forecast value), 'q' (quality descriptor), and 'unc' (uncertainty quantification), all typed using IEC 61850 CDCs (e.g., MV for measured value, SPS for single-point status). For AI-augmented forecasting infrastructure, the template integrates with ML pipeline outputs—supporting ingestion of ensemble forecasts, quantile regression outputs, or Monte Carlo simulation results—and includes built-in validation rules (e.g., temporal consistency checks, unit coherence, forecast horizon alignment) to ensure conformance before publishing via IEC 61850-8-1 (MMS) or IEC 61850-8-2 (GOOSE/SV) protocols. It also facilitates traceability through embedded digital signatures, version-controlled forecast provenance fields, and audit-ready logging sheets aligned with ISO/IEC 17025 and ENTSO-E Transparency Platform requirements.

📑 Key Components

1 Standardized Logical Node Mapping (WINDF, WINDP)
2 Forecast Metadata Schema (horizon, resolution, uncertainty bands, spatial coverage)
3 Automated Conformance Validation Engine (Excel-based rule checking)

🎯 Applications

  • Grid operator dispatch planning with certified forecast inputs
  • Third-party forecasting vendor certification and onboarding
  • Digital twin synchronization for wind farm performance analytics

📐 Key Formulas

Normalized Root Mean Square Error (NRMSE)

NRMSE = \frac{\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_i - \hat{y}_i)^2}}{y_{max} - y_{min}}

Quantifies forecast accuracy normalized by the observed power range; required for IEC 61400-25-4 forecast quality reporting

Pinball Loss (for Quantile Forecasts)

L_\tau(y, \hat{q}_\tau) = (y - \hat{q}_\tau) \cdot (\tau - \mathbb{I}(y < \hat{q}_\tau))

Evaluates calibration of probabilistic wind forecasts at quantile level τ; used to validate uncertainty representation per IEC 61400-25-4 Annex C

Forecast Horizon Consistency Check

Δt = t_{forecast} - t_{issue}

Verifies that reported forecast issuance time (t_issue) and valid time (t_forecast) align with declared horizon (e.g., 15 min–72 h); enforced in template validation logic

🔗 Related Concepts

IEC 61850 Information Modeling Probabilistic Forecasting for Renewables Grid Code Compliance (e.g., ENTSO-E RfG)

📚 References

#wind forecasting #IEC 61400-25 #interoperability #renewable energy standards #AI forecasting