Forecast Error Analysis for Wind Power Fluctuations
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Solution Overview
Problem
Current forecasting models for renewable energy sources like wind power are not accurate enough to capture temporal variations in energy fluctuations, leading to grid instability and increased reserve costs due to their reliance on time-independent error distributions that fail to account for correlated fluctuations.
Innovation Solution
A method and system that analyze time-dependent forecast errors by calculating time-scaling and scaling errors using a statistical structure derived from comparing generated and forecasted power trends, incorporating fractal scaling analysis and a modified forecast power trend with an exponentially decaying memory kernel to account for temporal correlations in wind power fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If forecast models use time-independent error distributions, then the model complexity is reduced, but the forecast accuracy deteriorates due to failure to capture temporal variations in energy fluctuations
Solution Approach 1:
The patent transforms the static, time-independent error distribution into a dynamic, time-dependent error distribution that evolves with temporal lags. The forecast model now incorporates time-varying error characteristics where the error distribution at time t depends on previous error realizations, allowing the model to adapt to changing fluctuation patterns while maintaining a structured approach through the use of lagged error terms.
Solution Approach 2:
The patent changes the parameters of the error distribution from fixed, time-independent values to time-dependent parameters that vary with lagged error terms. Specifically, the error distribution parameters (mean and variance) are modified to include temporal components, allowing the forecast model to capture evolving error patterns without requiring a complete restructuring of the forecasting framework.
2Measurement precision
If forecast models account for temporal variations in error, then the forecast accuracy is improved, but the model complexity increases due to need for time-dependent error distributions
Solution Approach 1:
The patent segments the error analysis into distinct temporal components by introducing lagged error terms (e.g., error at time t-1, t-2, etc.). This segmentation allows the complex time-dependent error structure to be broken down into manageable discrete lag components, each contributing to the overall error distribution in a structured and interpretable manner.
Solution Approach 2:
The patent incorporates feedback mechanisms where past forecast errors influence future error distributions. The lagged error terms create a feedback loop where the system learns from previous forecasting mistakes and adjusts the error distribution accordingly, allowing the model to adapt to temporal patterns in forecast accuracy without requiring external intervention.
3Stability of the object's composition
If distributed wind farms are used to smooth fluctuations, then the grid stability is improved, but the forecast error increases due to wild fluctuations in power entering the grid
Solution Approach 1:
The patent applies preliminary action by incorporating lagged error terms into the forecast model before new forecast errors are generated. By pre-adjusting the error distribution based on historical error patterns, the model proactively accounts for temporal variations in fluctuations, allowing for more accurate forecasting of distributed wind farm output and better preparation for grid stability management.
Data Source
AI summary
A system for determining (a) forecast error and/or (b) scaling error for wind power generation is provided, the system utilizing generated and forecast time series for power generation derived from wind and analyzing temporal correlations in the wind fluctuations to quantify: (a) the forecast error defined by deviations between the high frequency components of the forecast and generated time series, and (b) a scaling error defined by a degree that temporal correlations fail to be predicted for an accurate predictor of wind fluctuations. Wind fluctuations may exhibit multi-fractal behavior at the turbine level and/or may be rectified to a fractal structure at the grid level. A memory kernel may be used to reduce the forecast and scaling errors.