Adaptive Wind Forecasting via Ensemble Machine Learning
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Solution Overview
Problem
Current wind resource forecasting methods are limited in accuracy and adaptability, as they often rely on single neural networks, ignore turbine-level forecasts, and fail to effectively combine multiple forecasts across different models and time horizons, leading to suboptimal predictions.
Innovation Solution
A system and method that dynamically generate and combine multiple variants of machine learning models using historical and real-time data to derive a statistical model for improved wind resource forecasting, incorporating adaptive boosting techniques to optimize forecast accuracy across various predictors and time frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single neural network is used for wind resource forecasting, then the system complexity is reduced, but the forecast accuracy deteriorates due to inability to cover the entire parameter space
Solution Approach 1:
The patent divides the forecasting system into multiple neural networks, each specialized for different parameter ranges (e.g., different wind speed ranges, temperature ranges, or seasonal conditions). This segmentation allows each network to be optimized for its specific domain, improving overall forecast accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system dynamically changes parameters by selecting different neural networks based on current environmental conditions (wind speed, temperature, humidity, seasonal variations). This parameter-based selection ensures that the most appropriate model for the current conditions is used, thereby improving forecast accuracy without requiring a single overly complex model.
2Quantity of substance
If regional forecast information is used exclusively, then data availability is improved, but turbine-level forecast accuracy deteriorates due to ignoring turbine specific variations
Solution Approach 1:
The patent merges regional forecast data with turbine-specific measurements and characteristics by integrating multiple data sources into a unified forecasting framework. This combination allows the system to leverage the broad coverage of regional data while incorporating local turbine-specific variations, thereby improving turbine-level forecast accuracy without sacrificing data availability.
Solution Approach 2:
The system applies local quality by tailoring the forecasting approach to each turbine's specific characteristics (location, height, orientation, operational parameters) while still utilizing regional forecast information. This allows regional data to be adapted and refined for local conditions, improving turbine-level accuracy without ignoring the value of regional data availability.
3Measurement precision
If multiple machine learning models are dynamically generated and combined, then forecast accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-training and validating multiple machine learning models offline before deployment. During runtime, it dynamically selects and combines pre-validated models based on current conditions, which reduces real-time computational complexity while maintaining the accuracy benefits of multiple models. The heavy computational work is done in advance rather than during forecasting operations.
Solution Approach 2:
The system dynamically adapts by selecting and combining different models based on current environmental conditions and data availability. This dynamic approach allows the system to use simpler models when conditions permit and more complex models when needed, optimizing the balance between forecast accuracy and computational complexity in real-time rather than using a fixed complex architecture.
4Reliability
If adaptive boosting techniques are used to combine forecasts, then forecast reliability is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial adaptive boosting by selectively combining forecasts from the most relevant models based on current conditions rather than always using all available models. This partial application of the boosting technique maintains improved reliability through adaptive combination while reducing processing time by avoiding unnecessary computations from less relevant models in every forecasting instance.
Data Source
AI summary
A method and system are provided for determining at least one combined forecast value of non-conventional energy resources. An Input/output Interface receives an adaptively selected historical dataset and a current dataset from one or more predictive forecast models and/or measurements. An adaptive forecast module generates one or more variants of machine learning models to model the performance of the one or more predictive forecast models by training the one or more variants of machine learning models on the historical dataset. The adaptive forecast module correlates the current dataset with the historical dataset to adaptively obtain a filtered historical dataset. The adaptive forecast module evaluates the one or more variants of machine learning models on the filtered historical dataset. The adaptive forecast module derives a statistical model to determine the at least one combined forecast value by combining outputs obtained based on the evaluation.