Household Energy Prediction Model Clustering for Federated ESS AI
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Solution Overview
Problem
Energy prediction models for households suffer from performance errors due to limited data, leading to over-fitting when energy usage patterns change, as each household's model is learned independently without considering similar patterns in the same region.
Innovation Solution
An artificial intelligence apparatus generates and updates a federated model by clustering energy prediction models for households with similar usage patterns, using a processor to determine similarity and update the model based on received energy prediction models, thereby improving prediction accuracy and service performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If energy prediction models are learned independently for each household using limited local data, then model training is simple and fast, but prediction accuracy deteriorates when energy usage patterns change due to over-fitting
Solution Approach 1:
The patent merges multiple local energy prediction models from different households into a federated model by clustering households with similar energy usage patterns. This combining approach allows the system to leverage data from multiple sources while maintaining privacy, thereby improving prediction accuracy without requiring centralized data collection.
Solution Approach 2:
The patent segments the overall model training process into independent local model training phases at each household and a subsequent federated model construction phase. This segmentation allows parallel processing of local models while ultimately combining them to achieve better generalization performance and avoid over-fitting.
2Loss of information
If local models are trained using only household-specific data, then data privacy is maintained, but model performance deteriorates due to insufficient data diversity
Solution Approach 1:
The patent introduces a federated model as an intermediary that aggregates knowledge from multiple local models without requiring direct access to raw household data. This intermediary structure enables performance improvement through data diversity while maintaining privacy, as the federated model learns from clustered patterns across households without exposing individual household data.
3Reliability
If energy prediction models are updated frequently to adapt to changing usage patterns, then prediction accuracy improves, but computational resources and time are consumed
Solution Approach 1:
The patent performs preliminary clustering of households based on energy usage patterns before model training and updates. By pre-grouping households with similar patterns into clusters, the system reduces the computational complexity of frequent model updates, as changes can be propagated efficiently within clusters rather than recalculating entire federated models from scratch.
Data Source
AI summary
system (ESS), and a method for clustering an energy prediction model thereof and may be configured to check whether a federated model for determining the similarity with the energy prediction model for each household exists in the memory if an energy prediction model for each household is received, to determine the similarity between the energy prediction model for each household and the federated model if the federated model exists, and to cluster the energy prediction model for each household into the federated model according to the determined similarity to update the federated model.