Occupancy-Based Energy Management for HVAC Demand Response
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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 mobile interface for scheduling and demand response, enabling real-time energy monitoring and proactive energy use adjustments based on user location and energy pricing.
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 real-time energy pricing information, but consumers still lack active engagement and must manually curtail their energy use
Solution Approach 1:
The system enables energy-consuming devices to automatically adjust their operation based on real-time energy pricing and user-defined preferences. The energy management system acts autonomously to curtail or shift energy consumption without requiring manual user intervention, while still providing full visibility of energy usage and costs through the interface.
Solution Approach 2:
The system implements continuous feedback loops where real-time energy pricing data, consumption patterns, and cost information are fed back to both the user interface and the energy management controller. This feedback enables dynamic adjustment of energy consumption strategies based on current pricing conditions while maintaining user awareness and control.
2Productivity
If demand response systems force curtailment on customers to manage load levels, then utility companies can react to energy demand, but end users are inconvenienced
Solution Approach 1:
The system dynamically adjusts energy consumption strategies based on real-time pricing signals and grid conditions, while adapting to user preferences and historical behavior patterns. This dynamic approach enables flexible demand management that responds to changing conditions without imposing rigid, inconvenient curtailment schedules on users.
Solution Approach 2:
The system uses historical data and predictive algorithms to anticipate energy consumption patterns and proactively schedule energy-intensive tasks during low-cost periods. By performing preliminary analysis and pre-planning, the system can shift load without requiring last-minute user intervention or experiencing inconvenience from sudden curtailments.
3Measurement precision
If smart meters are deployed to measure and report consumption data, then real-time energy measurement is enabled, but communication and analytical infrastructure remains lacking for effective analysis and scheduling
Solution Approach 1:
The system segments the complex analytical infrastructure into distributed components, with intelligence embedded at multiple levels including the smart meter, local energy management controller, and cloud-based platform. This segmentation allows each component to handle specific analytical tasks locally, reducing the burden on centralized infrastructure while maintaining precise measurement and scheduling capabilities.
Data Source
AI summary
A method of managing at least one network device at a site includes providing a web-based control selector to manage a detection module to control the at least one network device at the site. The method includes enabling the detection module in response to an enabled setting of the web-based control selector, and initiating a change in an operating condition of the at least one network device at the site in response to the enabled detection module and based on a detected presence of a user at the site. The method proceeds by disabling the detection module in response to the disabled web-based control selector.