Intelligent Environmental Controller for Energy-Response Balancing
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Solution Overview
Problem
Existing intelligent controllers face challenges in dynamically balancing conflicting control goals and constraints in real-time, particularly in managing environmental parameters like temperature in smart-home environments, where energy efficiency and response time often conflict.
Innovation Solution
An intelligent controller that continuously monitors progress towards control goals and dynamically adjusts control strategies using sensor data and user inputs, employing auto-component-activation levels to optimize energy efficiency and response time, and incorporates advanced modeling techniques to predict remaining response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the controller activates all available systems to achieve control goals quickly, then response time is improved, but energy consumption increases
Solution Approach 1:
The controller dynamically adjusts control strategies based on real-time monitoring of system response progress. It transitions from static control schedules to adaptive control that modifies activation levels of systems based on how close control goals are being approached, optimizing the balance between response time and energy consumption.
Solution Approach 2:
The controller continuously monitors the response of controlled systems to determine progress toward control goals. This feedback mechanism enables the controller to assess whether systems are responding adequately and adjust activation levels accordingly, preventing unnecessary energy consumption from fully activated systems when partial activation suffices.
2Reliability
If the controller uses multiple systems simultaneously to achieve control goals, then control reliability is improved, but system complexity increases
Solution Approach 1:
The controller employs auto-component-activation levels that allow partial activation of systems based on the degree of progress toward control goals. Rather than requiring full activation of all systems, the controller uses proportional activation levels that match the urgency and progress of control objectives, reducing complexity while maintaining reliability.
Solution Approach 2:
The controller changes operational parameters by introducing auto-component-activation levels that modulate the intensity of system activation. This parameter adjustment allows the same physical systems to operate at different effectiveness levels, enabling reliable control without requiring additional system components or complex coordination.
3Measurement precision
If the controller continuously monitors system response to dynamically adjust control, then control precision is improved, but computational load increases
Solution Approach 1:
The controller uses pre-defined auto-component-activation levels and control strategies that are prepared in advance. Rather than performing complex real-time optimization calculations, the controller selects from pre-planned activation levels based on monitored progress, reducing computational load while maintaining control precision.
Solution Approach 2:
The monitoring and adjustment process is designed to be efficient and self-regulating. The controller monitors system response and automatically adjusts activation levels based on predefined criteria and progress metrics, minimizing the need for intensive computational processing while achieving precise control.
Data Source
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AI summary
The current application is directed to intelligent controllers that continuously, periodically, or intermittently monitor progress towards one or more control goals under one or more constraints in order to achieve control that satisfies potentially conflicting goals. An intelligent controller may alter aspects of control, dynamically, while the control is being carried out, in order to ensure that goals are obtained and a balance is achieved between potentially conflicting goals. The intelligent controller uses various types of information to determine an initial control strategy as well as to dynamically adjust the control strategy as the control is being carried out.