Wind Turbine Power Forecast Correction for Local Weather Effects
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Solution Overview
Problem
Existing weather forecasts for wind turbines are inaccurate due to not adequately accounting for local and occasional weather phenomena that affect wind speed at the height of the rotors, leading to systematic errors in power output predictions.
Innovation Solution
A method to create a power forecast for wind turbines by determining a normal forecast based on weather data, checking for specific weather phenomena that cause systematic errors, and applying correction rules to adjust the forecast, using criteria such as cloud cover, solar radiation, and wind speed at relevant altitudes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard weather forecasts are used for power prediction, then the forecasting process is simple and fast, but the accuracy deteriorates due to systematic errors from unaccounted local weather phenomena
Solution Approach 1:
The forecasting process is segmented into distinct phases: standard forecast generation, special weather phenomenon detection, and correction application. This allows the system to maintain simplicity for normal conditions while adding complexity only when needed for accurate prediction.
Solution Approach 2:
The system performs preliminary detection of special weather phenomena (radiation days, low-level jets, morning dips) before final power prediction. By identifying these phenomena in advance and applying pre-determined correction rules, the system improves accuracy without requiring complex real-time analysis during forecasting.
2Measurement precision
If local adjustment is made to weather forecasts, then accuracy for specific installation location improves, but the forecasting method becomes more complex
Solution Approach 1:
The system applies local quality by detecting special weather phenomena specific to the installation location and applying location-specific correction rules. The correction factors are determined based on local historical data and geographical characteristics, making the forecast locally optimized without requiring complete redesign of the forecasting system.
3Measurement precision
If correction rules for special weather phenomena are applied, then prediction accuracy improves, but the forecasting process requires additional steps
Solution Approach 1:
Correction rules for special weather phenomena are pre-determined and stored based on historical data analysis. When a special weather phenomenon is detected, the corresponding pre-prepared correction rule is immediately applied, avoiding time-consuming calculations and reducing the additional time required in the forecasting process.
Data Source
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AI summary
The invention relates to a method for creating a power forecast for at least one wind turbine (100) regarding the expected output power of the at least one wind turbine (100), wherein the at least one wind turbine (100) is installed at a site, and the method comprises the steps of determining a standard forecast, checking for at least one special weather phenomenon leading to a systematic error in the standard forecast, determining at least one correction rule for correcting the standard forecast if a special weather phenomenon was detected during the check, and correcting the standard forecast according to the at least one determined correction rule in order to obtain an adjusted power forecast.