Wind Power Output Forecasting via Machine Learning
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Solution Overview
Problem
The intermittent and uncontrollable nature of wind power generation poses challenges for integrating wind energy into the electric grid, leading to involuntary curtailment due to transmission congestion or oversupply, resulting in revenue loss and increased costs for grid operators.
Innovation Solution
A computer-implemented method using historical and real-time data to train machine learning models for predicting wind power output, allowing for accurate forecasting and curtailment management by determining oversupply conditions and adjusting wind turbine output accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wind power generation is increased to meet energy demand, then energy supply is improved, but grid stability deteriorates due to intermittency and uncontrollability
Solution Approach 1:
The system performs preliminary actions by forecasting wind power generation output in advance using machine learning models. This allows grid operators to anticipate intermittent production patterns and take proactive measures to maintain grid stability, such as scheduling conventional power plants or adjusting demand response programs before wind generation variability causes problems.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual wind power generation against forecasts and using this information to adjust grid operations. The machine learning models are trained on historical data and continuously refined, creating a feedback loop that improves prediction accuracy and enables better grid management decisions based on learned patterns of wind variability.
2Measurement precision
If machine learning models are used to predict wind power output, then forecasting accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the forecasting problem into multiple components by using separate machine learning models for different time horizons (short-term, medium-term, long-term forecasts). Each model is optimized for its specific time frame and can be independently trained and updated, reducing the overall computational burden compared to a single comprehensive model while maintaining high accuracy for each forecasting interval.
Data Source
AI summary
Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.