Energy Allocation Using Sub-Period Load Distribution Feedback
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Solution Overview
Problem
Current energy management systems rely on expert assessments based on past experience, leading to inefficiencies in energy usage optimization due to fluctuations in power generation performance, making it difficult to optimize overall energy efficiency and meet carbon reduction goals.
Innovation Solution
An energy allocation method and computing apparatus that utilizes machine learning algorithms to estimate electricity distribution and determine a recommended proportion of target energy based on historical consumption data, improving energy decision-making efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert assessment based on past experience is used to control energy transfer, then energy allocation decisions can be made, but the accuracy and optimization of energy usage is reduced due to fluctuations in power generation performance
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring actual power generation data and comparing it with historical consumption patterns. The system adjusts energy allocation recommendations based on the difference between expected and actual generation, enabling dynamic adaptation to power generation fluctuations while maintaining optimization accuracy.
Solution Approach 2:
The patent performs preliminary analysis of historical electricity consumption data across multiple sub-periods to establish baseline patterns and electricity distribution profiles. This preliminary action enables the system to proactively adjust allocation strategies before fluctuations impact energy usage, improving both accuracy and adaptability.
2Measurement precision
If historical electricity consumption data is analyzed across multiple sub-periods to determine electricity distribution, then energy allocation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the historical consumption period into multiple sub-periods (e.g., hourly, daily, or weekly intervals) and analyzes electricity distribution patterns within each segment. This segmentation allows the system to capture temporal variations in consumption behavior while managing computational complexity through structured, modular processing of each sub-period independently.
Solution Approach 2:
The patent applies partial action by focusing computational resources on analyzing the most significant sub-periods or consumption patterns that have the greatest impact on overall energy allocation. Rather than uniformly processing all historical data with equal depth, the system identifies and prioritizes key periods for detailed analysis, reducing unnecessary computational overhead while maintaining accuracy.
Data Source
AI summary
Disclosed is an energy allocation method and a computing apparatus. In the method, a historical electricity consumption of a target energy is obtained. A past period includes sub-periods, and the historical electricity consumption includes secondary electricity consumptions in sub-periods. An electricity distribution corresponding to the secondary electricity consumption in the sub-period is determined. The electricity distribution is an estimated electricity consumption distribution in sub-periods based on the electricity consumption in the sub-period. A recommended proportion of the target energy is determined according to a usage difference between the historical electricity consumption and the electricity distribution. The usage difference is a difference between the historical electricity consumption and an estimated sum. The estimated sum is a sum of estimated electricity consumptions in sub-periods under the electricity distribution, and the recommended proportion is a proportion of a recommended amount of the target energy to an electricity consumption of all energy.