Energy Grid Prediction Model Transfer for Faster Warm-Start
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Solution Overview
Problem
Current machine learning models for renewable energy systems require extensive training time and are context-specific, making it difficult to deploy them efficiently in different energy grid systems, such as wind and photovoltaic farms, without significant data collection and adaptation.
Innovation Solution
A system and method that utilize a center subsystem to train prediction models based on historical data from different energy grid systems, generating context-matching signatures to enable the transfer and adaptation of machine learning models between similar systems, allowing for a 'warm-start' of new models, thereby reducing deployment time and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using historical data from a specific energy grid system, then the model achieves high prediction accuracy for that system, but the model cannot be effectively applied to other different energy grid systems
Solution Approach 1:
The patent creates context-matching signatures that capture the essential characteristics of different energy grid systems. These signatures act as templates that allow a model trained on one system to be copied and adapted to other systems with similar contexts, rather than requiring complete retraining for each new system.
Solution Approach 2:
The patent transforms the model adaptation process by changing parameters based on context-matching signatures. Instead of fixed models, the system dynamically adjusts model parameters according to the contextual characteristics of the target energy grid system, enabling the same base model to adapt to multiple different systems.
2Measurement precision
If machine learning models are trained from scratch for each new energy grid system, then the model is specifically optimized for that system, but the deployment time and data collection requirements increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-training models on source energy grid systems and storing context-matching signatures. When a new deployment is needed, the system can quickly match the new system's context to existing pre-trained models, avoiding the time-consuming process of collecting data and training from scratch for each new system.
Solution Approach 2:
The system enables self-service model deployment by automatically matching new energy grid systems to appropriate pre-trained models using context-matching signatures. This automated process eliminates the need for manual data collection and model training for each deployment, allowing the system to serve itself with minimal human intervention.
3Measurement precision
If extensive historical data is collected for model training, then the model achieves better accuracy, but the time required for data collection and model training increases
Solution Approach 1:
The patent merges the training processes across multiple energy grid systems by using context-matching signatures to identify systems with similar characteristics. Instead of collecting and training on data from each individual system separately, the approach combines training data from multiple source systems that share similar contextual features, achieving comparable or better accuracy with reduced total training time.
Data Source
AI summary
Disclosed herein are embodiments for optimization of an energy grid system. First and second prediction models associated with a first energy grid system and a second energy grid system, respectively, may be trained based on historical data associated with each energy grid system. A prediction model basis may be created including the first and second prediction models. Training data associated with a third energy grid system may be input into each prediction model of the prediction model basis, and an accuracy of the prediction models may be evaluated to determine whether the prediction model basis is complete. When complete, a context-matching model may be trained based on subsequent energy grid systems until the context-matching model is determined to be sufficiently accurate. Then, the context-matching model may be used to identify a prediction model matching a new energy grid system, which may be used to warm-start the new energy grid system.