Central Plant Model Generation for Faster Building Energy Optimization
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Solution Overview
Problem
Generating central plant models for optimizing energy distribution across subplants in a central energy facility is time-consuming and difficult due to the complexity of managing multiple devices and resources, especially for buildings with many devices.
Innovation Solution
A system comprising a central plant optimizer wizard generator that creates equipment models, device layers, asset layers, and scaled load profiles using user inputs, allowing for the determination of optimal control decisions for devices within the central plant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual model generation is used for central plant devices, then model accuracy can be maintained, but the process becomes time-consuming and difficult
Solution Approach 1:
The system uses template-based model generation where pre-defined equipment models, device layers, and asset layers are copied and configured for central plant devices. This allows automated generation of accurate models without manual creation of each model from scratch, resolving the contradiction between model accuracy and generation time.
Solution Approach 2:
The system performs preliminary actions by pre-defining equipment models, device layers, and asset layers that can be automatically applied to central plant devices. This preliminary preparation enables rapid model generation while maintaining accuracy through standardized templates.
2Measurement precision
If detailed equipment models are generated for all devices, then optimization accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the central plant model into hierarchical layers: equipment models for individual devices, device layers for groups of devices, and asset layers for complete subplants. This segmentation allows detailed modeling where needed while managing complexity through organized structure.
Solution Approach 2:
The system adds a hierarchical dimension to the modeling approach, organizing devices across multiple levels (equipment → device layer → asset layer). This dimensional organization enables detailed optimization accuracy while managing complexity through structured hierarchy.
Data Source
AI summary
A control system for a central plant having devices serving energy loads of a building. The control system includes a central plant optimizer wizard generator that receives user input and generates a central plant model for the control system. The central plant optimizer wizard generator includes an equipment model generator that receives user input and generates equipment models for the devices in the central plant, a device layer generator that generates device layers, an asset layer generator that generates asset layers, and a scaled load profile generator that generates a scaled building load profile of the building using the asset layers and the user data. The central plant optimizer wizard generator generates the central plant model using the asset layers and the scaled building load profile. A demand response optimizer uses the central plant model to determine control decisions for the devices included in the central plant.