Method of controlling a device and device adapted to carry out said method
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Solution Overview
Problem
Existing heating installations, such as electric heating systems, face challenges in adapting to user presence and absence, leading to discomfort due to temperature inertia when switching between comfort and economy modes, and require manual programming which is inconvenient.
Innovation Solution
A method that employs a self-learning unit with a microcontroller to modify programming laws based on presence sensor data, adjusting temperature settings through intermediate modes to align with actual user presence or absence, thereby improving comfort and energy efficiency without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the installation switches from economy mode to comfort mode when a user is detected, then user comfort is improved, but temperature inertia causes discomfort during the switching period
Solution Approach 1:
The system performs preliminary heating actions before the user actually arrives. The microcontroller predicts the user's arrival time based on historical data and starts heating in intermediate modes beforehand, so that the comfort temperature is reached by the time the user enters, eliminating the temperature inertia discomfort.
Solution Approach 2:
The system dynamically adjusts the heating power and temperature setpoints based on the predicted user arrival time and current temperature. It transitions through multiple intermediate operating modes with varying power levels, optimizing the heating curve to reach comfort temperature exactly when needed, rather than using fixed economy or comfort modes.
2Loss of energy
If the installation uses fixed programming modes, then energy savings are achieved, but user comfort is compromised due to inability to adapt to actual usage patterns
Solution Approach 1:
The system continuously monitors actual user presence data from presence detectors and compares it with the predicted arrival times. The microcontroller uses this feedback to refine and update the programming law, improving the accuracy of predictions and optimizing the balance between energy savings and user comfort over time.
Solution Approach 2:
The system automatically learns and adapts to user patterns without requiring manual programming. The microcontroller self-adjusts the programming law based on accumulated presence data, eliminating the need for users to manually program schedules while maintaining both energy efficiency and comfort.
3Loss of energy
If manual programming is required to optimize heating schedules, then energy efficiency can be improved, but user convenience is reduced due to programming complexity
Solution Approach 1:
The system performs self-learning by automatically collecting presence data from detectors and using the microcontroller to generate and refine the programming law without any user intervention. This eliminates the need for manual programming while achieving optimized energy efficiency, as the system learns user patterns autonomously over time.
Data Source
Figure 1~2
Figure 3~5
AI summary
The method involves determining a programming rule of a device i.e. heating body (12), of an installation i.e. electric heater (10), over an operating cycle. Operation of the installation based on the programming rule is compared with effective utilization of the installation by a user. A discordance between a setpoint given by the programming rule of the device and the effective utilization of the installation by the user, is determined. The programming rule of the device is modified based on the effective utilization of the installation by the user. An independent claim is also included for an installation comprising a device control unit.