802.11 Proximity Detection for Automated Home Energy Control
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Solution Overview
Problem
Current energy management systems lack transparency and active participation from consumers in energy conservation, relying on passive methods like monthly bills and inadequate real-time data, and often inconvenience users with demand response programs that are not well-received.
Innovation Solution
An energy management system that includes a database for site report data, a processor to analyze thermostat settings and HVAC systems, and a network of devices for real-time energy monitoring and scheduling, enabling active energy management through a user interface and mobile applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If passive energy display technologies are provided to show current energy prices, then consumers can see energy information, but consumers still lack active participation and must manually curtail their use
Solution Approach 1:
The system enables energy-consuming devices to automatically adjust their operation based on real-time energy pricing and consumption patterns. The energy management system performs self-service by autonomously making curtailment decisions without requiring manual user intervention, while still achieving energy conservation goals through automated load management and scheduling.
Solution Approach 2:
The system implements continuous feedback loops where consumption data from smart meters is analyzed in real-time, and control signals are automatically sent back to energy-consuming devices. This feedback mechanism enables dynamic adjustment of energy usage based on current pricing, historical patterns, and system objectives, eliminating the need for manual monitoring and decision-making by consumers.
2Productivity
If demand response systems force curtailment on customers to manage load levels, then utility companies can control energy demand, but end users are inconvenienced
Solution Approach 1:
The system dynamically adjusts energy management strategies based on real-time conditions, user preferences, and consumption patterns. Rather than forcing fixed curtailment schedules, the system adapts its control actions to match user needs and grid conditions, enabling flexible demand management that responds to changing circumstances without imposing rigid restrictions on users.
Solution Approach 2:
The system performs preliminary analysis of consumption data, pricing signals, and user preferences to pre-determine optimal energy management strategies. By anticipating future energy needs and pricing conditions, the system can proactively schedule energy consumption and prepare control actions before peak demand periods occur, avoiding last-minute forced curtailment that would inconvenience users.
3Measurement precision
If smart meters are deployed to measure and report consumption data, then real-time energy data becomes available, but communication and analytical infrastructure remains lacking
Solution Approach 1:
The energy management system is designed to perform multiple functions within a single integrated platform: data collection from smart meters, real-time analysis of consumption patterns, pricing signal generation, device control, and user interface provision. This multi-functional approach eliminates the need for separate communication and analytical infrastructure components, reducing overall system complexity while maintaining precise measurement capabilities.
Data Source
AI summary
A method includes presenting a set location setting capable of being selected by a user within a graphical user interface (GUI) of a mobile device. The method proceeds by detecting on-site location information of the mobile device in response to a selection of the set location setting and detecting at least one on-site 802.11 based network in communication with the mobile device in response to a selection of the set location setting. The method proceeds by identifying an on-site IP address associated with the at least one on-site 802.11 based network, and then associating both the on-site location information and the on-site IP address with an on-site zone of a site associated with the mobile device. The method proceeds by altering an operating condition of a network device located at the site in response to a location of the mobile device relative to the on-site zone.