HVAC Profile Generation Using Weighted Sensor Data
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Solution Overview
Problem
Home automation systems often waste resources due to inefficient use of data sharing between network devices, leading to unnecessary operation of devices and inefficient resource allocation.
Innovation Solution
A computer-implemented method using a television receiver connected to a home automation system to receive user preferences, assign weights to HVAC sensors, generate an HVAC profile, and transmit it to the HVAC system, updating the profile based on recorded data to optimize energy distribution and usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If home automation devices operate without user preference data, then system simplicity is maintained, but resource waste increases and efficiency decreases
Solution Approach 1:
The system performs preliminary actions by collecting user preference data in advance (temperature preferences, room usage patterns, occupancy times) and pre-generating optimized HVAC profiles before actual operation. This allows the HVAC system to automatically adjust settings based on pre-analyzed user preferences rather than operating with default settings, thereby improving efficiency and reducing energy waste.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring HVAC sensor data (temperature readings, energy consumption) and comparing actual performance against user preferences. This feedback loop enables the system to learn from past operations, refine HVAC profiles, and progressively improve efficiency while minimizing energy waste through data-driven adjustments.
2Productivity
If HVAC systems operate without distributed sensor data, then system complexity is reduced, but resource allocation efficiency deteriorates
Solution Approach 1:
The system segments the HVAC control function by distributing temperature sensors across multiple rooms and assigning different weights to each sensor based on user preferences. Each sensor independently monitors its zone, and the central controller aggregates weighted data to determine optimal HVAC settings. This segmentation enables precise room-by-room resource allocation while maintaining manageable system complexity through modular sensor-controller architecture.
Solution Approach 2:
The HVAC system achieves multi-functionality by using a single centralized controller that performs multiple roles: collecting data from distributed sensors, analyzing user preferences, generating optimized profiles, and coordinating HVAC operations across different zones. This universal controller approach enables efficient resource allocation without requiring separate control systems for each room, thereby improving productivity while controlling complexity.
3Productivity
If user preferences are not integrated into HVAC control, then ease of operation is maintained, but energy distribution efficiency decreases
Solution Approach 1:
The HVAC system implements self-service by automatically collecting user preferences through various input methods (manual input, mobile app, voice commands) and using this data to autonomously generate and adjust HVAC profiles without requiring continuous user intervention. The system serves itself by learning from user behavior patterns and making automatic optimizations, thereby improving energy distribution efficiency while maintaining ease of operation through minimal user effort.
Solution Approach 2:
The system performs preliminary actions by pre-collecting user preference data and pre-generating optimized HVAC profiles before actual operation. This advance preparation enables the system to automatically adjust settings based on user preferences without requiring real-time user input, thereby improving energy distribution efficiency while maintaining operational simplicity through pre-configured intelligent control.
Data Source
AI summary
The present technology relates to systems and methods for control of home automation activity based on user preferences. More specifically, the present technology relates to using a home automation system to control home automation activity based on user preferences. Example embodiments include receiving an input from a user including a set of preferences, generating a user profile using the set of preferences, receiving data indicating that a mobile device has moved from a first location to a second location, transmitting the user profile to the mobile device for application to the home automation system, receiving data indicating that the mobile device has been at the second location for a period of time, comparing the period of time to a predetermined threshold period of time, and applying the home automation settings associated with the second location to the home automation system.