Ensemble Prediction Model for Electricity Demand Adaptation
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Solution Overview
Problem
Current electricity demand prediction models are difficult to apply universally due to varying demander types and frequent changes in demander groups, requiring extensive manual tuning and relearning, which is labor-intensive and inefficient.
Innovation Solution
An information processing apparatus that generates multiple prediction models based on various conditions and weights them to create an ensemble prediction model, allowing for accurate predictions with minimal tuning and adaptability to changes in demander groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single prediction model is used for all demanders, then device complexity is reduced, but prediction accuracy deteriorates due to varying demander types
Solution Approach 1:
The patent segments the demander population into multiple clusters based on similar consumption patterns. Instead of using a single prediction model for all demanders, the system creates separate prediction models for each cluster, allowing each model to be optimized for specific demander types while maintaining overall system manageability.
Solution Approach 2:
The patent dynamically adjusts model parameters based on the identified cluster characteristics. By changing parameters such as model complexity, feature selection, and weighting schemes according to the specific cluster being predicted, the system achieves high accuracy for diverse demander types without requiring completely different model structures.
2Measurement precision
If manual tuning is performed for each demander group, then prediction accuracy is improved, but productivity deteriorates due to enormous daily workload
Solution Approach 1:
The patent implements automated cluster identification and model selection algorithms that operate without manual intervention. The system automatically analyzes consumption patterns, identifies appropriate clusters, selects suitable prediction models for each cluster, and adjusts parameters dynamically, eliminating the need for manual tuning while maintaining high prediction accuracy.
Solution Approach 2:
The system automatically adjusts model parameters based on real-time data and cluster characteristics, replacing manual parameter tuning with automated parameter optimization algorithms that adapt to changing demander group compositions without requiring human expertise or time investment.
3Adaptability or versatility
If prediction models are frequently updated to adapt to changing demander groups, then adaptability is improved, but loss of time increases due to relearning requirements
Solution Approach 1:
The patent implements dynamic clustering that automatically adapts to changing demander group compositions. As new demanders join or leave the system, the clustering algorithm dynamically reorganizes clusters and adjusts model assignments without requiring complete relearning, allowing the system to maintain adaptability while minimizing relearning time through incremental updates.
Data Source
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AI summary
According to one approach, an information processing apparatus comprises: a first model generator configured to, generate a plurality of first prediction models for an objective variable based on data including an explanatory variable and the objective variable, and a plurality of model generation conditions; and a second model generator configured to weight the plurality of first prediction models based on differences between the objective variable and predicted values of the plurality of first prediction models calculated based on the explanatory variable, and to generate a second prediction model for the objective variable based on the weighted plurality of first prediction models.