Learning Model Selection for KPI-Adaptive Facility Control
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Solution Overview
Problem
Existing control systems for facilities with multiple pieces of equipment struggle to efficiently adapt control strategies to meet specific performance indicators (KPIs) due to the lack of effective model selection and switching mechanisms, leading to suboptimal operation.
Innovation Solution
A control support apparatus that utilizes a selection unit to identify a recommended learning model based on historical data and predicted future states to switch learning models dynamically, ensuring that control parameters align with user-defined KPIs, thereby optimizing facility performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single learning model is used for controlling facility equipment, then the control system is simple to implement, but it cannot adapt to different performance indicators (KPIs) and operational conditions
Solution Approach 1:
The control system is segmented into multiple specialized learning models, each trained to optimize specific KPIs (e.g., energy efficiency, production output, equipment maintenance). Instead of one general model, the system divides the control function across multiple models that can be selectively applied based on current operational priorities and conditions.
Solution Approach 2:
The control system achieves multi-functionality by implementing a model selection mechanism that can dynamically choose from multiple learning models. The system universally handles different KPIs and operational scenarios by selecting the appropriate specialized model, making the overall system adaptable to various conditions without requiring separate dedicated systems for each function.
2Adaptability or versatility
If multiple learning models are maintained for different KPIs, then adaptability to various operational conditions improves, but the computational resources and system complexity increase
Solution Approach 1:
Multiple learning models are pre-trained offline on historical data for different KPIs and operational scenarios. This preliminary action allows the models to be ready for immediate deployment without requiring intensive real-time computation. The heavy computational work of training is performed in advance, reducing online computational demands to merely selecting and applying the appropriate pre-trained model.
Solution Approach 2:
The system employs lightweight model selection mechanisms that require minimal computational resources compared to retraining or complex real-time optimization. The model selection process acts as a low-cost, efficient gateway that determines which pre-trained model to use, avoiding the need for expensive real-time computational resources while maintaining high adaptability.
3Productivity
If learning models are frequently switched to optimize KPIs, then facility performance improves, but system stability and reliability may deteriorate
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor operational conditions, KPI performance, and model effectiveness. This feedback loop enables intelligent decision-making about when to switch models, ensuring that switching occurs only when beneficial and conditions warrant the change. The feedback system prevents unnecessary or destabilizing switches while capturing performance improvements.
Solution Approach 2:
The model switching mechanism is designed to be dynamic yet controlled, adapting to changing operational conditions while maintaining system stability. The system dynamically evaluates whether switching models would improve performance based on current conditions, and only transitions when the evaluation indicates a beneficial change. This dynamic approach balances responsiveness to changing conditions with maintenance of operational stability.
Data Source
AI summary
Provided is an apparatus including: an acquisition unit which acquires state data regarding a facility; a selection unit which selects, among a plurality of learning models that output a control parameter to be applied to a control target in the facility, a recommended learning model recommended to be used for control of the control target in order to adapt a KPI regarding the facility to a reference condition, based on the state data acquired by the acquisition unit, in response to supply of state data regarding the facility; and an output unit which outputs identification information of the recommended learning model.