Multi-Room Climate Control Using Adaptive Thermal Behavior Models
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Solution Overview
Problem
Current climate systems face challenges in optimizing energy consumption by accurately anticipating and adapting to the thermal behavior of multiple rooms, especially due to interactions between rooms and external factors like solar radiation, which leads to inefficient energy use and discomfort.
Innovation Solution
A method utilizing centralized intelligence with a behavior model that anticipates thermal responses across multiple rooms, incorporating presence detection, meteorological data, and user habits to adjust temperature setpoints, minimizing energy consumption by optimizing the operation of air conditioning units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If the temperature is maintained at comfort level in all rooms continuously, then thermal comfort is ensured, but energy consumption increases
Solution Approach 1:
The system performs preliminary actions by anticipating future occupancy based on detected patterns (historical data, calendar events, user habits). Before a room is actually occupied, the system pre-adjusts the temperature to comfort levels, so that when occupancy occurs, the room is already at the desired temperature. This eliminates the need for reactive temperature changes that would consume more energy.
Solution Approach 2:
The system dynamically adjusts room temperatures based on real-time and predictive occupancy information. Instead of maintaining static comfort temperatures in all rooms, the system adapts temperatures room-by-room and time-by-time according to predicted occupancy patterns, thereby optimizing energy consumption while ensuring comfort when needed.
2Use of energy by moving object
If the temperature is reduced in unoccupied rooms to save energy, then energy consumption decreases, but thermal comfort is compromised when rooms are occupied
Solution Approach 1:
The system pre-heats or pre-cools rooms before predicted occupancy events. By using pattern recognition algorithms that analyze historical occupancy data, calendar information, and user habits, the system anticipates when a room will be occupied and adjusts the temperature in advance, ensuring comfort is ready before the user arrives while minimizing energy consumption during unoccupied periods.
Solution Approach 2:
The system continuously monitors actual occupancy, temperature conditions, and energy consumption, then uses this feedback to refine its predictive models. This allows the system to learn from past performance and improve its temperature adjustment timing and magnitude, optimizing the balance between energy savings and thermal comfort over time.
3Temperature
If the climate system reacts to temperature changes in one room, then local thermal comfort is improved, but energy consumption increases due to unnecessary adjustments in other rooms
Solution Approach 1:
The system segments the building into independent thermal zones (rooms), each with its own occupancy detection and temperature control. Instead of reacting to temperature changes across the entire building, the system independently manages each room based on its specific occupancy patterns and thermal characteristics, allowing localized optimization without affecting other rooms unnecessarily.
Solution Approach 2:
The system applies different temperature control strategies to different rooms based on their individual occupancy patterns, insulation properties, and usage characteristics. Each room receives customized temperature adjustments tailored to its specific needs and predicted occupancy, rather than applying uniform control across all spaces.
4Use of energy by moving object
If the system uses detailed pattern recognition and predictive algorithms, then energy optimization is improved, but device complexity increases
Solution Approach 1:
The system performs self-learning by automatically analyzing occupancy patterns, thermal responses, and energy consumption data without requiring manual programming or complex external configuration. The predictive algorithms adapt automatically to building-specific patterns and user behaviors, reducing the need for complex setup procedures and ongoing manual adjustments.
Solution Approach 2:
The system uses continuous feedback from sensors and occupancy detection to refine its predictive models in real-time. This adaptive learning approach allows the system to improve its energy optimization performance automatically as it gathers more data about the building's thermal behavior and occupancy patterns, reducing the need for pre-programmed complexity.
Data Source
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AI summary
The invention relates to a method for thermal regulation, with respect to a temperature setpoint, of a climate system comprising a plurality of climate devices (110, 111, 140, 141) in a room comprising several rooms, in the presence of a change in the temperature setpoint in one of said rooms, characterized in that it comprises the steps of: a. obtaining a thermal behavior model of the room, called the initial model; b. adapting the initial model to the actual thermal behavior of the room by analyzing the response to a stimulus of the climate system, to obtain an adapted model; c. calculating, from the adapted model, the temperature variation in each room as a function of the change in the operating conditions of one of the climate devices of the climate system; d.deduce from step c) the load conditions of each device in the climate system to respond to the change in temperature setpoint in the room; e. apply the load rates determined in step d) to each device in the climate system.