AI Climate Control Using Predictive Occupant Behavior Modeling
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Solution Overview
Problem
Current control systems and security systems lack the ability to accurately predict human behaviors and adapt to real-time events to meet user needs, often being reactive rather than proactive, leading to inefficiencies in energy use and security response.
Innovation Solution
A computerized framework utilizing AI, machine learning, and large language models to predict user needs and adjust climate and security settings dynamically based on past, present, and predicted data, integrating with smart home ecosystems for personalized control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional reactive control systems are used, then device complexity is reduced, but adaptability to user needs and real-time events deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting user needs and environmental changes before they occur. The AI model analyzes historical data, current sensor inputs, and contextual information to proactively adjust climate settings, security parameters, and energy management strategies in advance, transforming the system from reactive to predictive operation
Solution Approach 2:
The control system serves itself by using embedded sensors, AI processing capabilities, and automated decision-making algorithms to independently monitor, analyze, and adjust system parameters without requiring constant user intervention. The system learns from user behavior patterns and autonomously optimizes its operation
2Reliability
If traditional reactive control systems are used, then energy consumption is reduced, but user comfort and responsiveness deteriorate
Solution Approach 1:
The system anticipates user comfort needs by analyzing behavioral patterns, calendar data, and environmental forecasts to pre-adjust temperature, lighting, and security settings before users arrive or before conditions change, ensuring comfort is maintained without continuous high-energy operation
Solution Approach 2:
The system dynamically changes operational parameters such as thermostat setpoints, HVAC cycling patterns, and security system sensitivity based on real-time sensor data, user presence detection, and predicted needs, optimizing the balance between comfort and energy consumption
3Adaptability or versatility
If AI-based predictive control is implemented, then adaptability to real-time events improves, but device complexity increases
Solution Approach 1:
The control system performs multiple functions through a unified AI-based platform that integrates climate control, security management, energy optimization, and user behavior analysis into a single system, reducing the need for separate specialized subsystems and simplifying overall architecture
Solution Approach 2:
The system introduces an AI processing layer that acts as an intermediary between raw sensor data and control actuation, translating complex multi-source inputs into simplified decision-making signals that drive system responses without requiring direct complex wiring between all sensors and actuators
4Measurement precision
If traditional security systems are used, then false alarms are reduced, but security response accuracy to real threats deteriorates
Solution Approach 1:
The security system merges multiple detection modalities including sensors, cameras, access control data, and user behavior patterns into a unified analysis framework, combining diverse information sources to achieve more accurate threat assessment and reduce false alarms through contextual correlation
Solution Approach 2:
The system implements feedback loops where security events, user responses, and contextual information continuously refine the AI model's understanding of threat patterns, allowing the system to learn from past events and improve its discrimination between actual threats and false alarm conditions
Data Source
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AI summary
Disclosed are systems and methods that provide a novel framework for automatically and dynamically controlling and managing a climate system at a location based on past, present and/or predicted spatial, temporal, logical and/or user/device data at the location. The disclosed framework can maximize the capabilities of an implemented control system (e.g., climate and/or security) to leverage AI/ML and/or LLM predictions via novel mechanisms to understand the current and/or future needs of a user(s) within such location. The framework can enable automated notifications and/or responses to control a current environment of a location, as well as the desired environment of a user at and/or within the location, such that framework can provide, in concert with the control system at the location, a dynamically adaptive, automated system that can leverage generative software to control how climate and/or security systems control an environment tied to the comfort and protection of a locations' occupants.