Converged Control and Analytics System for Building Optimization
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Solution Overview
Problem
Current control systems and data analytics systems operate independently, failing to effectively integrate insights from data analytics into control processes, leading to suboptimal performance and inefficiencies in managing comfort and energy usage in residential and commercial buildings.
Innovation Solution
A converged system that combines control systems with data analytics, using IoT architecture to deploy edge-to-cloud gateways, cloud-based data storage, and analytics infrastructure, enabling automatic adjustments of control settings based on insights from large volumes of data, including predictive modeling and fault detection, to optimize operations without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If control systems and data analytics systems operate independently, then system simplicity is maintained, but performance optimization and energy efficiency deteriorate
Solution Approach 1:
The patent merges control systems and data analytics systems into a unified converged platform. The control system includes controllers that execute control algorithms, while the data analytics system includes analytics engines that process operational data. These systems are integrated through a common data infrastructure and communication protocols, enabling real-time exchange of control commands and analytics insights, thereby achieving performance optimization without excessive complexity
2Ease of manufacture
If control systems and data analytics systems operate independently, then implementation cost is reduced, but energy efficiency and operational optimization deteriorate
Solution Approach 1:
The converged control and analytics platform is designed to perform multiple functions through shared infrastructure. The same data collection mechanisms, processing pipelines, and communication protocols serve both control operations and analytics functions. This multi-functionality reduces redundant hardware and software components, lowering implementation costs while simultaneously enabling energy efficiency optimizations through integrated analytics that provide real-time insights for energy management
3Productivity
If control systems and data analytics systems are converged, then performance and energy efficiency are improved, but system complexity increases
Solution Approach 1:
The converged system is segmented into distinct but integrated functional modules: control system modules for executing control algorithms, data analytics system modules for processing and analyzing operational data, and shared infrastructure modules for data storage, communication, and coordination. This segmentation allows each module to be developed, deployed, and maintained independently while benefiting from the synergies of the converged architecture, thus improving performance without overwhelming system complexity
4Loss of information
If control systems and data analytics systems are converged, then operational insights are enhanced, but data processing requirements and computational load increase
Solution Approach 1:
The system performs preliminary data processing and filtering at the data collection stage, preprocessing operational data before it enters the main analytics pipeline. This preliminary action includes data validation, normalization, and initial filtering to remove irrelevant or erroneous data points. By preparing data in advance, the system reduces the computational load on downstream analytics engines while ensuring that high-quality, actionable insights are generated from the processed data
Data Source
AI summary
A structure having convergence of a control system and a data analytics system. The control system may involve input-output models of items, for example, homes, buildings and process plants. The system may use sensor data and scientific principles to implement control models. The models may help to precisely achieve a desired value of a parameter. The data analytics system may involve analysis of diverse data and may create insights of the items. The control system plus the data analytics system may close the loop between insights and on-premise controls of the items represented by the input-output models. The control and data analytics systems may merge the best of both worlds to solve difficult issues pertaining to the items. The structure may be for comfort control, energy reduction, and so on, having improved performance based on the convergence.