ML-Based Power Supply Management in Enclosed Spaces
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Solution Overview
Problem
Conventional power management systems face challenges in effectively utilizing backup power supplies during disruptions in the main power supply, leading to inefficient and cumbersome operations in enclosed spaces.
Innovation Solution
An electronic device equipped with memory and circuitry that determines disruptions in the main power supply, assesses power consumption of appliances, and applies a trained machine learning model to schedule the use of secondary power supplies, ensuring efficient power distribution during outages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional power management systems are used during main power supply disruption, then backup power supplies can provide basic power, but the utilization is tedious and troublesome with inefficient operations
Solution Approach 1:
The system automatically detects main power supply disruption, assesses appliance power consumption requirements, and schedules secondary power supply usage without user intervention. The electronic device independently manages the entire power transition process, making the system self-service oriented and eliminating manual operation complexity.
Solution Approach 2:
The system pre-assesses power consumption of electrical appliances before main power supply disruption occurs or immediately upon detection. By preparing power management schedules in advance based on predicted needs, the system enables smooth transition to backup power without operational delays or complexity during the actual disruption.
2Reliability
If secondary power supplies are used during main power supply disruption, then continuous power can be provided, but the management becomes complex and cumbersome
Solution Approach 1:
The system continuously monitors main power supply status, secondary power supply availability, and electrical appliance power consumption requirements. Based on this real-time feedback, the electronic device dynamically adjusts and optimizes the power management schedule, ensuring reliable power continuity while automatically managing system complexity through data-driven decision making.
Solution Approach 2:
The system changes operational parameters by transitioning from manual power management to automated scheduling based on detected power supply conditions. The electronic device adjusts power distribution parameters dynamically, optimizing the balance between reliability and complexity by adapting to real-time system states rather than following fixed complex procedures.
3Productivity
If manual management of backup power supplies is used, then users have control over power usage, but the process is tedious and time-consuming
Solution Approach 1:
The system automatically performs power consumption assessment, schedule generation, and power supply management without requiring user time or effort. The electronic device serves itself by independently handling all power management tasks, thereby maximizing productivity during disruption while eliminating the time loss associated with manual intervention.
Solution Approach 2:
The system replaces manual mechanical power management operations with automated electronic control and machine learning-based scheduling. This substitution eliminates the need for users to manually configure and manage backup power supplies, significantly improving productivity during power disruptions while reducing the time invested in power management tasks.
Data Source
AI summary
An electronic device and a method for management of power supply in enclosed space is provided. The electronic device determines first information indicating a disruption in a main power supply of a first enclosed space. The electronic device determines second information which indicates a power consumption of one or more electrical appliances in the first enclosed space based on the determined first information. The electronic device further determines third information associated with one or more secondary power supplies in the first enclosed space. The electronic device further applies a trained ML model on the determined first information, the second information, and the third information and determines scheduling information based on the application of the ML model. The electronic device further controls the one or more secondary power supplies to power the one or more electrical appliances, based on the determined scheduling information.