System and method for managing cooling load within a building

Solution Overview

Problem

Existing cooling systems in buildings face inefficiencies due to rule-based control methods that fail to adapt to dynamic thermal demands, leading to non-optimal energy consumption and under/over-cooling issues, requiring expert intervention for adjustments.

Innovation Solution

A system and method that utilizes sensors and virtual modeling to estimate operating variables, optimize cooling flowrates, and adjust dynamically based on real-time data to minimize energy consumption and maintain temperature ranges, capable of detecting and correcting off-design conditions without expert tuning.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If rule-based control methods are used to manage cooling systems, then the system operation is simplified, but the energy consumption increases and temperature control precision deteriorates

Engineering Contradiction:

Improvesystem operation simplicityVSAvoidenergy consumption

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The patent replaces rule-based mechanical control systems with a neural network-based intelligent control system. The neural network learns optimal cooling strategies from historical data and dynamically adjusts cooling flow rates based on real-time building conditions, eliminating the need for manual rule configuration while significantly improving energy efficiency and temperature control precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The cooling system becomes self-optimizing through the neural network controller that automatically adapts to changing building conditions without requiring expert intervention. The system continuously learns from operational data and autonomously adjusts cooling parameters to minimize energy consumption while maintaining comfort requirements.

Inventive Principle:

Principle #25Self-service

2Ease of operation

If rule-based control methods are used to manage cooling systems, then the system operation is simplified, but the temperature control precision deteriorates

Engineering Contradiction:

Improvesystem operation simplicityVSAvoidtemperature control precision

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent replaces rule-based mechanical control systems with a neural network-based intelligent control system. The neural network learns optimal cooling strategies from historical data and dynamically adjusts cooling flow rates based on real-time building conditions, eliminating the need for manual rule configuration while significantly improving energy efficiency and temperature control precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements continuous feedback loops where sensors monitor actual temperature conditions and occupancy, the neural network processes this information, and the controller adjusts cooling flow rates accordingly. This closed-loop control ensures precise temperature maintenance while adapting to changing conditions.

Inventive Principle:

Principle #23Feedback

3Manufacturing precision

If expert tuning is required for cooling system optimization, then the control precision can be improved, but the device complexity and operational difficulty increase

Engineering Contradiction:

Improvecooling control precisionVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The cooling system becomes self-optimizing through the neural network controller that automatically adapts to changing building conditions without requiring expert intervention. The system continuously learns from operational data and autonomously adjusts cooling parameters to minimize energy consumption while maintaining comfort requirements.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces rule-based mechanical control systems with a neural network-based intelligent control system. The neural network learns optimal cooling strategies from historical data and dynamically adjusts cooling flow rates based on real-time building conditions, eliminating the need for manual rule configuration while significantly improving energy efficiency and temperature control precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentEP4657199A1System and method for managing cooling load within a building

Publication Date: 2025.12.03 SIEMENS AG

Figure 1A
Figure 1B
Figure 2

AI summary

The invention relates to a system and method for managing cooling load within a building wherein a cooling system (102) is installed. The method comprises obtaining, by a processing unit (125), cooling load data associated with one or more locations within the building from one or more sources (105). Further, values of a plurality of operating variables associated with the cooling system (102) are estimated using the cooling load data. Based on the determined values of the plurality of operating variables, optimized cooling flowrate is computed for each of the one or more locations using an optimizer, wherein at least one objective associated with the optimizer is to minimize a total energy consumption by the cooling system. Furthermore, instructions for implementing the optimized cooling flowrate for each of the one or more locations are generated and transmitted to one or more target devices (115) operably coupled to the cooling system (102).