Neighboring-Site Ramp Prediction for Short-Term Renewable Forecasts
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Solution Overview
Problem
Current wind and solar power forecasting methods face challenges in accurately predicting short-term power variations, leading to increased imbalance penalties and operational inefficiencies due to the variable and uncertain nature of renewable energy sources.
Innovation Solution
The implementation of a system that uses ramp predictors and decision trees to improve forecast accuracy by analyzing correlations between nearby renewable energy sites, generating lagged power measurements, and applying these to correct forecast errors in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods are used for variable power generation assets, then the forecasting system is simple and easy to operate, but the forecast accuracy is insufficient leading to increased imbalance penalties
Solution Approach 1:
The patent introduces ramp predictors as intermediary variables that mediate between historical power measurements and forecasted power output. These predictors capture ramp events (sudden changes in power generation) and use them to correct forecast errors, thereby improving accuracy without requiring complete redesign of the forecasting system
Solution Approach 2:
The system implements feedback by calculating forecast errors (difference between predicted and actual power generation) and using these errors to train decision tree models. The models continuously learn from past errors and apply corrections to future forecasts, creating a closed-loop system that improves over time
2Measurement precision
If more data from nearby sites is collected and analyzed, then forecast accuracy improves through better correlation analysis, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the forecasting problem by focusing on specific temporal patterns (ramp events) rather than analyzing all aspects of power generation data. By identifying and separately handling ramp events through dedicated predictors, the system improves accuracy while avoiding the need to process and model every detail of the complex multi-site data
Solution Approach 2:
The system performs preliminary action by pre-calculating ramp predictors from historical data and storing them for future use. This allows the forecasting model to quickly access pre-processed information about ramp patterns without performing complex real-time analysis, reducing computational burden during actual forecasting operations
3Reliability
If real-time forecast error correction is applied using decision trees, then imbalance penalties are reduced through more accurate forecasts, but computational processing time and resources increase
Solution Approach 1:
The decision tree models are trained in advance on historical forecast errors and ramp predictor data. This preliminary training phase allows the models to be ready for rapid inference during actual forecasting operations, reducing real-time computational requirements while maintaining the ability to provide accurate error corrections
Solution Approach 2:
The system uses computationally efficient decision tree models that can be quickly trained and applied without requiring extensive computational resources. These models provide sufficient accuracy for real-time correction while being much faster and less resource-intensive than complex neural networks or other heavy machine learning approaches
Data Source
AI summary
An example method includes, at a weather forecast time, determining a lag between a target renewable energy site and a first nearby site for which respective power measurements are correlated, selecting a first forecast look-ahead time, determining if the first forecast look-ahead time is less than or equal to the lag, determining a series of lagged power measurements at a time of forecast which constitute a series of correlation-based forecasts for power generation at the target site based on the lag, generating a set of ramp predictors incorporating correlation-based forecasts from the first site and the first forecast look-ahead time, receiving power forecast errors, applying sets of decision trees to the predictors and the power forecast errors to obtain predicted forecast errors, and generating second power forecasts for the set of look-ahead times of the target site based on the first power forecasts and the predicted forecast errors.