Temperature Regulation Modeling for Energy-Efficient Component Selection

The system performs preliminary actions by simulating and evaluating multiple temperature regulation system configurations before actual deployment. Machine learning models predict energy consumption patterns and component performance under various weather conditions and thermal loads, allowing the selection of optimal system designs in advance, thereby improving energy efficiency without requiring complex real-time adjustments during operation.

The patent creates virtual copies of temperature regulation systems through machine learning models that replicate the behavior of physical systems. These digital twins simulate energy consumption, component interactions, and system performance under different operating conditions, enabling efficient evaluation and optimization of multiple configurations without building and testing each physical prototype, thus improving energy efficiency while managing system complexity.

The patent replaces traditional physics-based thermodynamic simulations with machine learning models that have been trained on such data. This substitution uses statistical patterns learned from comprehensive simulations to predict system behavior, maintaining high design optimization accuracy while dramatically reducing computational time during the evaluation and deployment phases.

Comprehensive system simulations are performed in advance to train machine learning models. The models learn from extensive simulation data covering various weather conditions, thermal loads, and component configurations. Once trained, these models can quickly evaluate new designs without requiring time-consuming full simulations, thus achieving both high optimization accuracy and reduced computational time for subsequent design iterations.