Power Usage Prediction System for Peak Load Management
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Solution Overview
Problem
Contemporary electricity billing systems face challenges in accurately predicting peak power usage, leading to expensive billing and potential electricity supply disruptions, as they rely on manual analysis of power usage data without efficient prediction methods.
Innovation Solution
A power usage prediction system that includes a power measurement unit, a modeling unit for generating data sets from time-series measurement data, and a prediction unit that uses real-time data to forecast power usage, determining if it exceeds a limit value without requiring expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of power usage data is used, then expert knowledge can be applied, but the process is time-consuming and lacks automation
Solution Approach 1:
The system enables self-service through automated data collection, processing, and prediction generation. The power usage prediction system automatically measures power consumption data, generates time-series datasets, trains prediction models, and outputs predictions without requiring expert intervention at each step, thereby resolving the contradiction between accuracy and time consumption
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated computational system. The prediction unit substitutes expert manual analysis with algorithmic processing that automatically trains models on historical data and generates predictions, eliminating the time-consuming nature of manual work while maintaining or improving prediction accuracy
2Measurement precision
If expert analysis is used to determine peak power usage, then accurate identification is possible, but expert intervention is required continuously
Solution Approach 1:
The prediction system performs self-service by automatically identifying peak power usage through trained prediction models. The system autonomously processes historical data, generates predictions, and identifies peak usage periods without continuous expert intervention, achieving both high accuracy and full automation
Solution Approach 2:
The system implements feedback mechanisms where prediction results are continuously refined based on actual power usage data. The prediction unit compares predicted values with actual measurements and adjusts model parameters accordingly, enabling automatic peak identification that improves over time without expert intervention
3Measurement precision
If separate variables are used for power usage prediction, then prediction accuracy may improve, but system complexity increases
Solution Approach 1:
The prediction unit serves multiple functions: it trains prediction models on historical data, generates power usage predictions, identifies peak usage periods, and refines models based on feedback. This multi-functionality achieves comprehensive prediction accuracy without requiring separate specialized systems for each task, thereby maintaining system simplicity
Data Source
AI summary
A power usage prediction system and method determines whether or not a predicted power usage exceeds a limit value using modeling data for power usage. The system includes a power measurement unit for measuring power usage at a certain time interval; a modeling unit for generating a plurality of data sets by grouping a certain number of a plurality of measurement data indicating the measured power usage in time series, storing the last measurement data of the data set as a modeling output, and storing measurement data other than the modeling output of the data set as a modeling input; and a prediction unit for inputting real-time data measured in real time in the power measurement unit into the modeling unit in time series, and predicting the power usage after the real-time data by corresponding the real-time data with the plurality of data sets.