Electrical Load Model Calibration for Privacy-Preserving Regional Forecasting
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Solution Overview
Problem
Existing electrical load modeling techniques face challenges in achieving accurate predictions while preserving customer privacy, as they often require access to sensitive meter data which is protected by privacy regulations, and local models lack the benefit of broad training data.
Innovation Solution
A decentralized approach where a generalized load curve prediction model is trained using local data at a regional server system, with model updates sent to a central server for iterative improvement, ensuring privacy by not transmitting actual meter data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generalized load curve prediction model is trained using broad training data, then prediction accuracy is improved, but customer privacy is compromised due to access to sensitive meter data
Solution Approach 1:
The system segments the training process into two distinct phases: (1) centralized training of a generalized model using aggregated data from multiple utilities, and (2) decentralized fine-tuning of local models using regional data. This segmentation allows the generalized model to benefit from broad training data while local models maintain privacy by training only on regional data without exposing sensitive meter information.
Solution Approach 2:
The patent introduces an intermediary mechanism where only model parameters (weights and biases) are transmitted between the centralized and decentralized systems, never the actual meter data. The generalized model serves as an intermediary that captures broad patterns, while local models serve as intermediaries that adapt to regional characteristics without exposing raw data.
2Measurement precision
If local models are trained using regional data, then region-specific prediction accuracy is improved, but the models lack the benefit of broad training data
Solution Approach 1:
The system performs preliminary action by first training a generalized model on broad training data from multiple utilities before deploying it to local systems. This pre-trained model contains general patterns and knowledge that are then transferred to local models, which subsequently fine-tune these parameters using regional data. This preliminary training ensures local models start with broad knowledge before adapting to specific regions.
Solution Approach 2:
The system implements feedback mechanisms where local models are evaluated using regional test data, and performance metrics are used to guide further fine-tuning. The feedback loop allows local models to iteratively improve their region-specific accuracy while maintaining the general patterns learned from the centralized model, balancing both broad and local knowledge.
3Object-affected harmful factors
If a decentralized approach is used for model training, then customer privacy is protected, but the training process becomes more complex
Solution Approach 1:
The system merges the advantages of both centralized and decentralized approaches by combining a centralized training phase for the generalized model with decentralized fine-tuning phases for local models. This hybrid merging allows privacy protection through decentralized data handling while maintaining training efficiency through centralized coordination of the overall process and model architecture.
Data Source
AI summary
Methods, systems, and apparatus, including medium-encoded computer program products, for configuring location-specific electrical load models while preserving privacy. A first machine learning model configured to predict electrical load curves of an electrical utility grid can be obtained from a server. Load values associated with a particular region of the electrical utility grid can be obtained. The load values can be applied as calibration input to the first machine learning model to produce first adjustment parameters for the first machine learning model. The first adjustment parameters can be provided to the server.