Transfer learning model for newly setup environment
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Solution Overview
Problem
Existing smart thermostatic systems require extensive data input and heavy computing power to train machine learning models, limiting their accuracy to similar environments and making it inefficient to adapt to new environments.
Innovation Solution
Implementing a reinforcement or adaptive learning model that transfers a pretrained machine learning model from one thermostatic system to another, using temperature and occupancy data to fine-tune the model for improved operation in new environments, leveraging deep neural networks with LSTM or transformer architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained from scratch in a new thermostatic environment, then the model accuracy for that specific environment is improved, but the time and computing power required for training increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models in source environments before deployment. The pre-trained models capture general thermal characteristics and patterns that can be transferred to new environments, eliminating the need to train from scratch and significantly reducing training time while maintaining accuracy.
Solution Approach 2:
The patent uses copying by transferring the pre-trained model parameters and structures from source environments to target environments. This allows the new thermostatic system to inherit learned thermal patterns and behaviors without retraining, saving substantial computational resources and time while adapting to new conditions through fine-tuning.
2Measurement precision
If a machine learning model is trained from scratch in a new thermostatic environment, then the model accuracy for that specific environment is improved, but the computing power requirements increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models in source environments before deployment. The pre-trained models capture general thermal characteristics and patterns that can be transferred to new environments, eliminating the need to train from scratch and significantly reducing training time while maintaining accuracy.
Solution Approach 2:
The patent uses copying by transferring the pre-trained model parameters and structures from source environments to target environments. This allows the new thermostatic system to inherit learned thermal patterns and behaviors without retraining, saving substantial computational resources and time while adapting to new conditions through fine-tuning.
3Power
If a thermostatic system uses a pretrained machine learning model from another environment, then the computing power and time requirements are reduced, but the initial model accuracy for the new environment decreases
Solution Approach 1:
The patent applies local quality by adapting the pre-trained model to local environmental characteristics through fine-tuning. The model retains general thermal patterns from source environments while learning environment-specific nuances, achieving both computational efficiency and local accuracy by combining transferred knowledge with local adaptation.
Solution Approach 2:
The patent uses parameter changes by adjusting model parameters during fine-tuning to adapt to the new environment. The pre-trained model's parameters are modified based on local thermal characteristics, occupancy patterns, and environmental factors, enabling the model to maintain high accuracy while benefiting from transfer learning's computational efficiency.
4Measurement precision
If extensive data is collected and trained in a new thermostatic environment, then the model becomes more accurate for that environment, but the system complexity and implementation difficulty increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models in source environments before deployment. The pre-trained models capture general thermal characteristics and patterns that can be transferred to new environments, eliminating the need to train from scratch and significantly reducing training time while maintaining accuracy.
Data Source
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AI summary
A smart thermostatic system is disclosed that applies one or more of a reinforcement and/or adaptive learning model for a new environment with the trained model from another environment so as to initiate status of a thermostatic device. In one example, the thermostatic system uses a pretrained machine learning model that is transferred from a first thermostatic system to a second thermostatic system in a similar sub-environment. Temperature data and other data collected by the thermostatic device is used to fine-tune and train the pretrained model to learn, predict, and better adjust the operation of the thermostatic system.