Building automation system with an energy optimization builder and generic data model designer
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Solution Overview
Problem
Current HVAC system control algorithms require iterative rebuilding and deployment, making the development and testing process inefficient, as changes often necessitate a full rebuild, which is time-consuming and resource-intensive.
Innovation Solution
The Algorithms As A Service (A3S) platform enables efficient and robust development of building management algorithms by using metadata packages, a context, and a kernel, allowing for runtime injection and avoiding redeployment, with features like a sequencer for data collection and storage, customizable dashboards, and disaster recovery mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If iterative rebuilding and deployment of control algorithms is performed, then the system can be updated and improved, but the development and testing process becomes inefficient and time-consuming
Solution Approach 1:
The control algorithm is divided into modular components that can be independently developed, tested, and deployed. This segmentation allows specific algorithm portions to be updated without requiring complete system rebuilds, thereby reducing development time while maintaining adaptability.
Solution Approach 2:
Algorithm components are pre-compiled and prepared in advance as deployable modules. This preliminary action enables rapid deployment of updated algorithms without requiring full system reconstruction, thus reducing the time lost in iterative development cycles.
2Stability of the object's composition
If full rebuild of the algorithm is performed for every change, then system consistency is maintained, but the process becomes resource-intensive and inefficient
Solution Approach 1:
The algorithm system is segmented into independent, versioned modules that can be updated selectively. This maintains system consistency through controlled module integration while avoiding the need for complete rebuilds, thereby improving development efficiency.
Solution Approach 2:
Algorithm modules are copied and deployed as independent executable units. This copying mechanism allows multiple versions to coexist and be tested independently, maintaining system consistency while enabling efficient iterative development without full rebuilds.
3Adaptability or versatility
If iterative testing and deployment is performed, then algorithm improvements can be made, but the process requires repeated builds which is inefficient
Solution Approach 1:
The build process is segmented into modular compilation steps for each algorithm component. This reduces build process complexity by allowing selective rebuilding of only modified modules, while maintaining the capability for iterative testing and deployment of improvements.
Solution Approach 2:
Algorithm modules are pre-compiled and packaged as deployable units before testing. This preliminary compilation action simplifies the iterative testing process by eliminating repeated full builds, thereby reducing build process complexity while enabling continuous improvement.
Data Source
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AI summary
A method for executing computations in parallel for a building management system of a building includes receiving a computing job request to determine values for one or more particular properties, receiving a property model indicating dependencies between a plurality of properties, the plurality of properties including the one or more particular properties, wherein the plurality of properties include building data for the building, and generating one or more computing threads based on the property model, wherein each computing thread includes a sequence of computations for determining values for the plurality of properties. The method further includes executing the computing threads in parallel to determine the values for the particular properties.