Method and device for internet-based optimization of parameters of heating control
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Solution Overview
Problem
HVACR systems often consume excessive energy due to non-optimum parameterization of control algorithms, leading to inadequate heating or cooling, as users lack the expertise to adjust control parameters effectively, resulting in inefficient operation and increased energy costs.
Innovation Solution
An internet-based method for optimizing control parameters of HVACR systems by detecting and analyzing various temperature and radiation data points, using a central server to calculate and transmit optimized parameters to the system, allowing for automatic or user-driven adjustments to ensure efficient energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control parameters are manually adjusted by users, then control quality can be improved, but this requires expert knowledge and increases operational complexity
Solution Approach 1:
The system automatically optimizes control parameters by collecting operational data, analyzing it through algorithms, and adjusting parameters without requiring user expertise. The HVAC system serves itself by performing the optimization function that previously required skilled operators.
Solution Approach 2:
The system continuously monitors operational data including temperature deviations, energy consumption, and system performance, then uses this feedback to automatically adjust control parameters. This closed-loop approach improves control quality while eliminating the need for manual expert intervention.
2Ease of operation
If automatic adjustment using identification and adaptation method is used, then users are freed from optimization tasks, but system complexity increases
Solution Approach 1:
A server acts as an intermediary between the HVAC system and the optimization algorithms. The server collects data from multiple systems, performs centralized analysis, and distributes optimized parameters back to individual systems. This distributes complexity to a dedicated component rather than embedding it throughout the entire system.
Solution Approach 2:
The optimization system serves multiple HVAC systems simultaneously through the central server, which performs data collection, analysis, and parameter optimization for various buildings. This multi-functional approach amortizes the complexity across multiple applications.
3Device complexity
If heating system operates with fixed parameters, then system operation is simple, but energy consumption increases due to non-optimum parameterization
Solution Approach 1:
The system transitions from static fixed parameters to dynamic adaptive parameters that automatically adjust based on real-time operational conditions. The control parameters evolve over time as the system learns from accumulated data, enabling energy optimization without requiring complex manual intervention.
Solution Approach 2:
The system automatically modifies control parameters such as supply temperature, flow rates, and timing based on analyzed operational data. These parameter changes optimize energy consumption by matching system operation to actual building requirements rather than relying on fixed conservative settings.
4Ease of operation
If control parameters are not optimized, then system operation remains simple, but thermal power supply becomes inadequate at extreme temperatures
Solution Approach 1:
The system automatically detects when optimization is needed and performs parameter adjustments to ensure adequate thermal power supply during extreme temperatures, without requiring user awareness or intervention. The system serves itself by monitoring and correcting its own performance.
Data Source
AI summary
The present invention relates to a method for determining a set of optimized control parameters (Θk) of a closed-loop controller (3) or an open-loop controller for an HVACR (heating, ventilation, air conditioning and refrigeration) system (2). In a first method step, an outside temperature (TA), an actual room temperature (TR) of a room (9), a supply temperature (TVL), a predefined target room temperature (TR,W), and a predefined target supply temperature (TVL,W) are detected. From the detected measured values (TA, TR, TR,W, TVL, TVL,W) and a time (tk) of detection data packet (Dk) is generated, which is transmitted via an internet connection to a server (8) where the data packet (Dk) is stored in a storage medium (6, 7) connected to the server (8). In the next method step, a set of optimized control parameters (Θk) is calculated on the basis of the measured values (TA, TR, TR,W, TVL, TVL,W) of the transmitted and stored data packet (Dk) and on the basis of measured values (TA, TR, TR,W, TVL, TVL,W) of a plurality of further data packets (D0 . . . k) generated at an earlier time (t. . . k) of a specified period (Δt) and/or at least one of a plurality of previously determined sets of optimized control parameters (Θk-1) by executing a calculation algorithm on the server (8). In the following method step, the calculated set of optimized control parameters (Θk) is stored in the storage medium (6, 7) connected to the server (8) and is transmitted via the internet connection to the closed-loop controller (3) or the open-loop controller of the HVACR system (2) or to a user (B) of the HVACR system (2).