Introducing New Time Series Integration: Sharpening Wind Resource Analysis for Bankable Results

February 14, 2025 Articles & PR | Product Update | Offshore | Onshore 5 min read time

A More Precise Way to Estimate Wind Output

In wind energy development, accurate P50 estimation, LCOE calculation, and yield forecasting hinge on how well you model the wind over time. Historically, most platforms have relied on Weibull distributions—an approach that, while useful, has limitations in capturing temporal variability.

To address this, Youwind has introduced a major update to its platform: Time Series Integration. This enhancement enables developers to model wind resource inputs more realistically, improving confidence in financial modeling for wind energy.

Key Enhancements in the Wind Temporal Analysis Module

The update introduces three major advancements:

Scenario-wide impact: Time series results now directly update P50, LCOE, and related KPIs within Youwind scenarios
Mean of Monthly Means method: Replaces the older “median year” method to better represent seasonally unbalanced datasets
Curtailment loss simulation: Added to existing wake loss modeling for a more complete net yield assessment

How It Works: From Raw Time Series to Bankable KPIs

Within the Wind Resource section, users can now activate the “Use Time Series Analysis” toggle. This shifts the source of analysis from Weibull-based statistical inputs to actual time-stamped wind speed data.

Key workflow highlights:

Upload time series to the Time Series Locations library
Frequency distribution charts are generated and used for yield calculations
Scenario production values auto-update with gross and net yields from the time series
All outputs are reflected in the Wind Temporal Analysis and Finance sections for traceability

This shift is especially valuable in early-stage bankable yield assessment, where site-specific measurements (e.g., Lidar or met mast campaigns) are limited to 1–3 years.

Methodology Update: Why Monthly Means Matter

Previously, Youwind derived long-term production from the median year in the dataset—effective, but sensitive to unbalanced short-term data. The new approach:

Computes average power per timestamp
Aggregates to a monthly mean value
Multiplies by average month length to get monthly energy
Offers more robust results for shorter time series—a common scenario in real-world measurements

This method improves reliability when using limited field data, and helps align yield projections with real-world seasonal variations.

Why This Matters: Reducing Uncertainty in Wind Energy Planning

Incorporating time series into your modeling pipeline enhances:

Data-driven decisions in wind energy
Scenario comparison for wind farms
Bankability of yield assessments with better representation of variability
Transparent alignment of energy modeling with financial projections

The update is especially relevant for offshore wind farm developers navigating complex meteorological regimes or aiming to de-risk early-stage investment discussions.

Final Thought: Wind Doesn’t Blow Statistically—It Blows in Time

By integrating granular, timestamped wind data, Youwind’s platform helps bridge the gap between resource variability and financial confidence. The Time Series Integration empowers teams to move beyond approximations—and toward reliable, scenario-based planning.

Written by James Milne, Technical Advisor

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