Consumer Grouping for Scalable Power Demand Prediction
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Solution Overview
Problem
Existing systems face increased processing loads for situation understanding and prediction as the number of consumers grows, and they struggle to acquire requested data without interpolating missing data types, leading to inefficiencies in power transaction and transfer monitoring.
Innovation Solution
A data substitution system that groups consumers with similar profiles and behaviors, using a representative consumer to predict power supply and demand, and substitutes this prediction for member consumers, thereby reducing processing loads and enabling efficient data acquisition across multiple consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual situation understanding and prediction processing is executed for each consumer, then prediction accuracy is improved, but processing load increases with the number of consumers
Solution Approach 1:
The patent groups multiple consumers into consumer groups based on similarity of their power supply and demand patterns. Instead of executing prediction processing for each individual consumer, the system performs prediction once per group and applies the result to all members, thereby reducing processing load while maintaining acceptable prediction accuracy through the similarity-based grouping approach.
Solution Approach 2:
The patent creates representative consumer profiles that capture the essential characteristics of each consumer group. These representative profiles are used to generate prediction results that are then copied and applied to all consumers within the group, eliminating the need to perform separate prediction processing for each individual while preserving the predictive information.
2Loss of information
If data is interpolated for missing data types, then data completeness is improved, but data reliability deteriorates due to estimation errors
Solution Approach 1:
The patent introduces representative consumers as intermediaries between the data collection system and individual consumers. When data is missing for a member consumer, the system retrieves data from the representative consumer's profile instead of interpolating, thereby obtaining complete and reliable data without introducing estimation errors associated with interpolation methods.
3Measurement precision
If consumer groups are formed based on detailed profile matching, then prediction accuracy is improved, but group formation complexity increases
Solution Approach 1:
The patent extracts only the essential and most influential characteristics from detailed consumer profiles to form groups. Rather than analyzing all available profile data in detail, the system identifies and uses key features that most significantly impact power supply and demand patterns, thereby reducing group formation complexity while preserving prediction accuracy.
Data Source
AI summary
A data substitution system includes: a communication unit configured to receive data related to power transfer from a consumer server provided in each of a plurality of consumers; a group formation processing unit configured to select, when a predetermined facility related to the power transfer is included in profile information of a consumer included in the received data related to the power transfer, the consumer as a representative consumer, select, as a member consumer, a consumer in which a degree of coincidence with the profile information of the selected representative consumer and with behavior information which is related to power supply and demand of the representative consumer and which is associated with the data related to the power transfer is equal to or higher than a predetermined threshold, and form the selected representative consumer and member consumer as one group; a prediction substitution processing unit configured to predict a situation related to the power supply and demand using a predetermined prediction algorithm for the selected representative consumer in the formed group and set a result of the prediction as a prediction result for the member consumer; and a data substitution processing unit configured to substitute the result of the prediction as a prediction result for a member consumer included in the group.