Cloud-Based Building Energy Modeling for HVAC Control
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Solution Overview
Problem
Current HVAC control systems rely on manual, trial-and-error methods for setting and maintenance, leading to delays and inaccurate results, with little coordination between control elements, resulting in inefficient energy management in buildings.
Innovation Solution
A cloud-based building systems management apparatus that includes a processor to generate configuration information for facility environmental controllers by modeling building infrastructure using weather and usage data, simulating energy usage scenarios, and calculating optimal control settings through a building energy simulator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error methods are used for HVAC control setting and maintenance, then human judgment and experience can be applied, but the process leads to delays and inaccurate results
Solution Approach 1:
The system creates a digital twin (virtual model) of the building infrastructure that replicates the physical building's characteristics, allowing virtual testing and optimization of HVAC control settings without affecting the actual building. This digital copy enables accurate simulation and prediction of energy consumption under different control scenarios.
Solution Approach 2:
The system performs preliminary simulation and optimization of HVAC control settings in the virtual model before implementing them in the actual building. By pre-testing multiple control scenarios virtually and selecting the optimal configuration, the system eliminates trial-and-error adjustments and reduces on-site configuration time.
2Reliability
If manual methods are used for HVAC control configuration, then flexibility in adjustments is possible, but coordination between control elements is poor
Solution Approach 1:
The digital twin replicates the entire building infrastructure and HVAC control system, enabling comprehensive simulation of interactions between different control elements. This virtual representation allows the system to automatically optimize coordinated control strategies across heating, cooling, ventilation, and other subsystems.
Solution Approach 2:
The system automatically adjusts multiple control parameters simultaneously based on simulation results, optimizing the coordinated operation of HVAC elements. By changing parameters in the virtual model and evaluating their combined effect on energy consumption and control coordination, the system achieves reliable multi-element coordination without manual intervention.
3Productivity
If traditional HVAC management is used, then existing systems can operate, but energy management efficiency is low
Solution Approach 1:
The digital twin creates a virtual replica of the building infrastructure that enables sophisticated energy management simulations and optimizations without adding physical complexity to the actual building systems. The complex computational models run in the virtual environment, while the physical building receives simplified control commands.
Solution Approach 2:
The digital twin acts as an intermediary between the complex energy management algorithms and the physical HVAC systems. It translates complex simulation results into actionable control settings, bridging the gap between advanced energy optimization techniques and existing building infrastructure without requiring direct modification of the physical systems.
Data Source
AI summary
Example apparatus, systems and methods for cloud-based simulation and control of building systems are disclosed and described herein. An example apparatus includes a control center including a processor configured to implement a modeler to generate a model of a target building infrastructure based on weather data, usage data, and building properties information. The example processor is configured to implement a building energy simulator to simulate energy usage for the target building using the model and scenario parameters. The example simulator is to simulate a plurality of scenarios with respect to the model to determine the configuration information for the one or more facility environmental controllers at the target building. The example simulator is to calculate a value associated with each simulated scenario and facilitate comparison of the values to generate the configuration information corresponding to a selected value to be selected from the values associated with the simulated scenarios.