Wind Farm Power Forecasting via Real-Time Data Compensation
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Solution Overview
Problem
Accurate forecasting of power output from wind farms and individual wind turbines is challenging due to their intermittent nature, making it difficult for operators to effectively participate in day-ahead energy markets.
Innovation Solution
A method and system that collect actual operational data and site information to generate a model-based power output forecast, which is adjusted using real-time data through a compensator module, potentially employing neural networks or machine learning to account for deviations and improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wind turbines operate in intermittent conditions due to changing wind speed and weather, then adaptability to environmental conditions is improved, but power output predictability deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and processing meteorological data and operational parameters in advance to generate power output forecasts before the actual generation occurs. This allows operators to predict future power output despite intermittent wind conditions, resolving the contradiction between adaptability to changing conditions and predictability of output.
Solution Approach 2:
The system implements feedback by continuously monitoring actual operational data and comparing it with forecasted values, then using this information to refine and adjust future predictions. This feedback loop improves predictability while maintaining adaptability to varying wind conditions through iterative learning and correction.
2Measurement precision
If a physics-based model is used to generate power output forecasts, then theoretical accuracy is improved, but real-time prediction accuracy deteriorates due to model deviations
Solution Approach 1:
The system introduces a compensator module as an intermediary between the physics-based model and the final forecast output. This compensator learns and corrects deviations from actual operational data, combining the theoretical accuracy of physics-based models with real-time accuracy by mediating between model predictions and actual observations.
Solution Approach 2:
The system dynamically adjusts model parameters based on learned deviations from actual operational data. By changing parameters to reflect real-world conditions and model inaccuracies, the system maintains theoretical accuracy while improving real-time prediction accuracy through adaptive parameter modification.
3Measurement precision
If comprehensive operational data and site information are collected, then forecast accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system applies multi-functionality by using a single integrated platform that performs multiple functions: data collection from multiple sources, data processing, model execution, deviation learning, and forecast generation. This universal approach improves forecast accuracy through comprehensive data utilization while managing complexity through consolidated processing architecture.
4Measurement precision
If a compensator module using neural networks or machine learning is implemented, then forecast accuracy is improved, but system complexity increases
Solution Approach 1:
The compensator module acts as an intermediary layer that learns from operational data and corrects model deviations without requiring complete system redesign. This intermediary approach improves forecast accuracy through machine learning while managing system complexity by adding a modular component rather than fundamentally changing the entire system architecture.
Data Source
AI summary
The present disclosure is directed to a system and method for forecasting a farm-level power output of a wind farm having a plurality of wind turbines. The method includes collecting actual operational data and/or site information for the wind farm. The method also includes predicting operational data for the wind farm for a future time period. Further, the method includes generating a model-based power output forecast based on the actual operational data, the predicted operational data, and/or the site information. In addition, the method includes measuring real-time operational data from the wind farm and adjusting the power output forecast based on the measured real-time operational data. Thus, the method also includes forecasting the farm-level power output of the wind farm based on the adjusted power output forecast.