WindCTD Forecasting Model for Wind Power Prediction
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Solution Overview
Problem
The variability and unpredictability of wind speed and direction pose challenges in accurately forecasting electrical power generation from wind farms, impacting operators, consumers, and regulators, as existing methods often rely on proprietary data and are computationally costly.
Innovation Solution
A deep learning time-series forecasting model, WindCTD, utilizing open-source wind farm output power and meteorological data from weather stations, employs a CNN-Transformer-Dense architecture to capture regional spatial and weather patterns, improving predictive accuracy and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained using proprietary data and complex architectures, then prediction accuracy improves, but computational cost and device complexity increase
Solution Approach 1:
The patent replaces expensive, complex proprietary deep learning models with simpler, open-source alternative models that achieve comparable accuracy. The system uses readily available meteorological data and simpler computational approaches, eliminating the need for expensive proprietary data and complex model architectures while maintaining prediction quality.
Solution Approach 2:
The patent changes the parameters of the forecasting system by switching from proprietary data sources to open-source meteorological data, and from complex deep learning architectures to simpler models. This parameter change reduces computational requirements and device complexity while maintaining or improving prediction accuracy through better feature engineering and data selection.
2Measurement precision
If existing forecasting methods use proprietary data and complex models, then prediction accuracy improves, but computational cost increases
Solution Approach 1:
The patent employs computationally efficient, open-source models that require less energy and computational resources compared to complex proprietary deep learning systems. The simpler model architecture and use of readily available data reduce the computational burden while maintaining prediction accuracy.
Solution Approach 2:
The patent extracts and uses only the essential features from open-source meteorological data, eliminating the need for computationally intensive processing of proprietary data. By focusing on key meteorological parameters and using efficient feature engineering, the system achieves accurate predictions with reduced computational cost.
3Device complexity
If simpler forecasting models are used, then device complexity reduces, but prediction accuracy deteriorates
Solution Approach 1:
The patent compensates for model simplicity by optimizing other parameters, including using high-quality open-source meteorological data, implementing sophisticated feature engineering, and selecting appropriate time-series forecasting methods. These parameter changes ensure that simpler models achieve prediction accuracy comparable to or better than complex proprietary systems.
Data Source
AI summary
Renewable energy provides humanity with a means of harvesting natural phenomena. However, the generating means are typically non-linear and the natural phenomenon variable such that the resulting electrical output is similarly variable and difficult to predict impacting their operators as well as consumers, regulators, planners, government bodies, etc. It would be beneficial therefore to provide engineers, infrastructure operators, regulators, planners etc. with a framework that allows for the electrical output from specific elements of infrastructure to be predicted. This framework being implantable, for example, through software processes and methods either associated with the elements of infrastructure or independent from the elements of infrastructure.