Power Demand Forecasting With Real-Time Error Correction
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Solution Overview
Problem
Current power management systems lack reliability in predicting power consumption due to the accumulation of errors when using past data to forecast future consumption, leading to inefficiencies and inaccuracies in demand management.
Innovation Solution
A closed-loop power management system employing a full-time scale prediction method with machine learning models for both short-term and long-term predictions, coupled with real-time error tracking and model adjustment, to improve prediction accuracy and reduce the difference between actual and predicted demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prediction data from previous time points is used to calculate future power consumption data, then the prediction process can be performed, but accumulation of errors occurs leading to reduced reliability
Solution Approach 1:
The patent segments the prediction task into multiple independent prediction models, each trained on specific historical time periods. Instead of using sequential prediction where errors accumulate, the system divides the prediction process into separate segments (different time periods) that can be independently calculated and compared with actual data, preventing error propagation across the entire prediction chain.
Solution Approach 2:
The patent changes the parameter of time period segmentation by training separate prediction models on different historical time periods. This parameter change allows the system to capture temporal variations in power consumption patterns while maintaining prediction independence, thereby improving both reliability and accuracy without suffering from error accumulation.
2Measurement precision
If multiple prediction models are used for different time periods, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the prediction task into multiple models trained on different time periods, which improves accuracy by capturing temporal patterns. The complexity is managed by organizing these segmented models in a structured manner where each model handles a specific time period, making the overall system complexity tractable and manageable.
Solution Approach 2:
The prediction system serves multiple functions: it predicts power consumption for different time periods, evaluates prediction accuracy, identifies high-quality power users, and optimizes power supply plans. This multi-functionality justifies the use of multiple prediction models, as the system gains comprehensive capabilities that outweigh the increased complexity.
3Measurement precision
If real-time error tracking is implemented, then prediction accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-training multiple prediction models on historical data from different time periods before actual prediction is needed. This preliminary preparation allows the models to be ready for rapid inference, reducing the processing time required during real-time operation while maintaining high prediction accuracy through error tracking.
Data Source
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AI summary
The present disclosure relates to a power management method and apparatus, a computing device, a medium, and a product. The power management method comprises: a monitoring step: in real time, acquiring and storing power consumption data of a user; a prediction step: on the basis of the acquired power consumption data, using a pre-stored first prediction model to predict a first predicted power consumption of a first time period, and using a pre-stored second prediction model to predict a second predicted power consumption of each second time period included in the first time period; an error calculation step: on the basis of the acquired real-time power consumption data, from the first second time period to the last second time period included in the first time period, sequentially executing the following processing on each second time period: calculating a first error between the actual power consumption of the second time period and the second predicted power consumption of the second time period, and calculating a second error between the total actual power consumption from the first second time period to the current second time period and the sum of the each of the second predicted power consumptions from the first second time period to the current second time period; and an adjustment step: when the first error is greater than a first predetermined threshold and/or the second error is greater than a second predetermined threshold, adjusting the power supply plan or the power demand of the user.