Short-Term Wind Forecasting Using Atmospheric Boundary Layer Physics
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Solution Overview
Problem
Current short-term wind forecasting methods, such as the 'naive predictor' or persistence model, are inaccurate and fail to capture rapid wind speed changes, leading to inefficiencies and reliability issues in wind energy production, particularly in managing power supply and demand, as they lack physical anchors and rely on simplistic models.
Innovation Solution
A system and method that uses a physically-based approach by extrapolating slowly-varying large-scale atmospheric pressure force gradients to predict future wind speeds, anchored in a robust physical model of wind variability in the atmospheric boundary layer, providing more accurate short-term wind forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If persistence model is used for short-term wind forecasting, then forecast simplicity is maintained, but forecast accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary physical model (atmospheric boundary layer equations) that connects observed wind speeds to forecasted wind speeds. Instead of directly using persistence model assumptions, the invention uses physical laws as intermediaries to transform historical data into accurate short-term forecasts, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The invention changes the fundamental parameter being forecasted from direct wind speed persistence to wind speed variability based on physical model predictions. By transforming the forecasting approach from assuming constant wind speed to using physically-based wind speed variability, the system achieves both operational feasibility and high accuracy.
2Measurement precision
If meso-scale models are used for wind forecasting, then medium-term and long-term forecast accuracy is improved, but short-term forecast accuracy deteriorates due to model response time
Solution Approach 1:
The patent segments the forecasting problem into two distinct parts: using meso-scale models for medium-term and long-term forecasts, and using a separate physics-based statistical model for short-term forecasts. This segmentation allows each model to operate in its optimal time range, avoiding the response time limitation of meso-scale models for short-term predictions while maintaining their accuracy for longer periods.
Solution Approach 2:
The invention applies partial action by using only the necessary components of physical models (atmospheric boundary layer equations) rather than full meso-scale models for short-term forecasting. This partial application of physical principles provides sufficient accuracy for short-term predictions without the computational overhead and response time delays of complete meso-scale modeling.
3Reliability
If backup generation capacity is maintained to compensate for forecast errors, then power supply reliability is improved, but energy efficiency deteriorates due to wasted backup generation
Solution Approach 1:
The patent implements feedback by using high-accuracy short-term wind forecasts to continuously adjust backup generation levels. The forecast system provides real-time information about expected wind production, allowing operators to dynamically reduce or maintain backup capacity based on actual forecasted conditions, thereby maintaining reliability while minimizing energy waste from unnecessary backup generation.
Data Source
AI summary
A system and method for performing novel wind forecasting that is particularly accurate for forecasting over short-term time periods, e.g., over the next 1-5 hours. Such wind forecasting is particularly advantageous in wind energy applications. The disclosed method is anchored in a robust physical model of the wind variability in the atmospheric boundary layer (ABL). The disclosed method approach leverages a physical framework based on the unsteady dynamics of earth's atmosphere, and drives forecasting as a function of previously-observed atmospheric condition data observed at the same location for which a wind forecast is desired.