DNV Validation

Validate your turbulence intensity adjustments against industry-standard DNV RP-0661 criteria.

Quick Validation

from tact.validation import validate_dnv_rp0661

# After running adjustment
validation = validate_dnv_rp0661(
    adjusted_data=results["adjusted_data"],
    reference_col="ref_ti",
    adjusted_col="adjTI_RSD_TI",
    wind_speed_col="ref_ws",
    bin_col="bins",
    criteria_type="LV"  # Load Verification
)

# Check results
print(f"MRBE: {validation['overall']['MRBE_%'].iloc[0]:.2f}%")
print(f"RRMSE: {validation['overall']['RRMSE_%'].iloc[0]:.2f}%")
print(f"Pass: {validation['overall']['pass'].iloc[0]}")

Understanding DNV Criteria

LV (Load Verification)
PC (Power Curve)

Load Verification CriteriaUsed for turbine load calculations and site assessments.Acceptance Criteria:

MRBE (Mean Relative Bias Error) ≤ 5%
RRMSE (Relative Root Mean Square Error) ≤ 15%

validation = validate_dnv_rp0661(
    adjusted_data=results["adjusted_data"],
    reference_col="ref_ti",
    adjusted_col="adjTI_RSD_TI",
    wind_speed_col="ref_ws",
    bin_col="bins",
    criteria_type="LV"
)

When to use:

Site assessment studies
Turbulence load calculations
Due diligence evaluations

Power Curve CriteriaStricter requirements for power performance testing.Acceptance Criteria:

More stringent than LV
Per IEC 61400-12-1 standards

validation = validate_dnv_rp0661(
    adjusted_data=results["adjusted_data"],
    reference_col="ref_ti",
    adjusted_col="adjTI_RSD_TI",
    wind_speed_col="ref_ws",
    bin_col="bins",
    criteria_type="PC"
)

When to use:

Power performance testing
Energy yield assessments
Warranty verification

Validation Workflow

Run Adjustment

First, complete your turbulence adjustment:

from tact import TACT
from tact.utils.load_data import load_data
from tact.utils.setup_processors import setup_processors

data = load_data("data.csv")
bp, tp, sp = setup_processors("config.json")
data = bp.process(tp.process(data))

tact = TACT()
results = tact.adjust(data, "ss-sf", {"split": True, "config_path": "config.json"})

Run Validation

Validate the adjusted results:

from tact.validation import validate_dnv_rp0661

validation = validate_dnv_rp0661(
    adjusted_data=results["adjusted_data"],
    reference_col="ref_ti",
    adjusted_col="adjTI_RSD_TI",
    wind_speed_col="ref_ws",
    bin_col="bins",
    criteria_type="LV"
)

Check Results

Review overall and per-bin validation:

# Overall metrics
print("Overall Validation:")
print(validation['overall'])

# Per-bin details
print("\nPer-Bin Validation:")
print(validation['by_bin'])

Generate Plots

Create professional validation visualizations:

from tact.visualization import plot_dnv_validation

plot_dnv_validation(
    validation_results=validation,
    adjusted_data=results["adjusted_data"],
    reference_col="ref_ti",
    adjusted_col="adjTI_RSD_TI",
    unadjusted_col="rsd_ti",
    wind_speed_col="ref_ws",
    bin_col="bins",
    method_name="SS-SF",
    output_dir="output/plots"
)

Validation Output

Overall Metrics

The validation['overall'] DataFrame contains:

Metric	Description	Target (LV)
`MRBE_%`	Mean Relative Bias Error	≤ 5%
`RRMSE_%`	Relative Root Mean Square Error	≤ 15%
`pass`	Whether criteria are met	True
`N`	Number of observations	-

Per-Bin Metrics

The validation['by_bin'] DataFrame shows results for each wind speed bin:

# Example output
   bin  N  MRBE_%  RRMSE_%  pass
0  4-5  120   3.2    12.1   True
1  5-6  245   4.1    13.5   True
2  6-7  312   2.8    11.2   True
...

Complete Example

from tact import TACT
from tact.utils.load_data import load_data
from tact.utils.setup_processors import setup_processors
from tact.validation import validate_dnv_rp0661
from tact.visualization import plot_dnv_validation

# 1. Load and process
data = load_data("data.csv")
bp, tp, sp = setup_processors("config.json")
data = bp.process(tp.process(data))

# 2. Run adjustment
tact = TACT()
results = tact.adjust(data, "ss-sf", {"split": True, "config_path": "config.json"})

# 3. Validate
validation = validate_dnv_rp0661(
    adjusted_data=results["adjusted_data"],
    reference_col="ref_ti",
    adjusted_col="adjTI_RSD_TI",
    wind_speed_col="ref_ws",
    bin_col="bins",
    criteria_type="LV"
)

# 4. Print results
overall = validation['overall']
print(f"\n{'='*50}")
print(f"DNV RP-0661 LV Validation Results")
print(f"{'='*50}")
print(f"MRBE:  {overall['MRBE_%'].iloc[0]:6.2f}% (target: ≤5%)")
print(f"RRMSE: {overall['RRMSE_%'].iloc[0]:6.2f}% (target: ≤15%)")
print(f"Pass:  {overall['pass'].iloc[0]}")
print(f"N:     {overall['N'].iloc[0]}")
print(f"{'='*50}\n")

# 5. Generate plots
plot_dnv_validation(
    validation_results=validation,
    adjusted_data=results["adjusted_data"],
    reference_col="ref_ti",
    adjusted_col="adjTI_RSD_TI",
    unadjusted_col="rsd_ti",
    wind_speed_col="ref_ws",
    bin_col="bins",
    method_name="SS-SF",
    output_dir="output/plots"
)
print("Validation plots saved to output/plots/")

# 6. Save validation results
validation['overall'].to_csv("validation_overall.csv", index=False)
validation['by_bin'].to_csv("validation_per_bin.csv", index=False)

# Validate multiple methods
methods = ["ss-sf", "ssws", "sswsstd"]
validation_results = {}

for method in methods:
    # Run adjustment
    results = tact.adjust(data, method, {"split": True, "config_path": "config.json"})

    # Validate
    validation = validate_dnv_rp0661(
        adjusted_data=results["adjusted_data"],
        reference_col="ref_ti",
        adjusted_col="adjTI_RSD_TI",
        wind_speed_col="ref_ws",
        bin_col="bins",
        criteria_type="LV"
    )

    validation_results[method] = validation

# Compare
import pandas as pd
comparison = pd.DataFrame({
    method: {
        'MRBE_%': val['overall']['MRBE_%'].iloc[0],
        'RRMSE_%': val['overall']['RRMSE_%'].iloc[0],
        'Pass': val['overall']['pass'].iloc[0]
    }
    for method, val in validation_results.items()
}).T

print(comparison.sort_values('MRBE_%'))

Visualization

The plot_dnv_validation function generates:

Scatter Plots

Before vs after adjustment comparison
1:1 reference line
Regression fit line
R² correlation coefficient

DNV Acceptance Plots

MRBE and RRMSE per wind speed bin
Acceptance criteria threshold lines
Pass/fail indication by color

Error Analysis

Residual plots
Bias distribution
Error by wind speed bin

Interpreting Results

Passing Validation
Failing MRBE
Failing RRMSE
Failing Both

Both MRBE and RRMSE meet criteria

MRBE:  3.21% (target: ≤5%)   ✅
RRMSE: 12.45% (target: ≤15%) ✅
Pass: True

What this means:

Adjustment is successful
Results meet DNV standards
Data suitable for its intended use

Next steps:

Generate final report
Save validated results
Proceed with site assessment

❌ MRBE exceeds 5% threshold

MRBE:  8.5% (target: ≤5%)    ❌
RRMSE: 13.2% (target: ≤15%)  ✅
Pass: False

What this means:

Systematic bias remains
Adjustment method may not be optimal

Troubleshooting:

Try different adjustment method (e.g., SS-SF instead of SSWS)
Check data quality and synchronization
Review per-bin results for specific problem ranges
Verify configuration column mappings

❌ RRMSE exceeds 15% threshold

MRBE:  3.1% (target: ≤5%)    ✅
RRMSE: 18.9% (target: ≤15%)  ❌
Pass: False

What this means:

High scatter/random error
Data may have quality issues

Troubleshooting:

Check data quality filtering
Verify sensor calibration
Review RSD-reference distance (should be less than 200m)
Increase dataset size if possible
Check for measurement synchronization issues

❌ Both metrics fail

MRBE:  12.3% (target: ≤5%)   ❌
RRMSE: 25.7% (target: ≤15%)  ❌
Pass: False

What this means:

Significant data or methodology issues
Adjustment not effective

Troubleshooting:

Verify data quality and completeness
Check time synchronization between sensors
Try all available methods (compare results)
Review raw data correlations
Consider data collection issues

Getting Started

Guides

Advanced

DNV Validation

Quick Validation

Understanding DNV Criteria

Validation Workflow

Validation Output

Overall Metrics

Per-Bin Metrics

Complete Example

Visualization

Interpreting Results

Next Steps

Visualization Guide

Troubleshooting

​Quick Validation

​Understanding DNV Criteria

​Validation Workflow

​Validation Output

​Overall Metrics

​Per-Bin Metrics

​Complete Example

​Visualization

​Interpreting Results

​Next Steps

Visualization Guide

Troubleshooting

Quick Validation

Understanding DNV Criteria

Validation Workflow

Validation Output

Overall Metrics

Per-Bin Metrics

Complete Example

Visualization

Interpreting Results

Next Steps