Asset behavior modeling
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Solution Overview
Problem
Asset settings configured by users based on knowledge or written instructions often result in inefficiencies and decreased performance, particularly in air handler units where fan speed settings are misconfigured, leading to increased energy consumption and suboptimal air quality.
Innovation Solution
A system that utilizes sensor data and asset scenario data to execute a model representing predicted asset behavior, providing insights and optimizing settings such as minimal fan speed, through data analytics and machine learning techniques, to improve efficiency and cost savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If asset settings are configured by users based on knowledge or written instructions, then ease of operation is improved, but asset performance and energy efficiency deteriorate
Solution Approach 1:
The system enables self-service by allowing the asset to automatically optimize its own settings through sensor data collection and machine learning model execution, eliminating the need for manual user configuration while achieving optimal performance automatically
Solution Approach 2:
The system dynamically changes operational parameters such as fan speed by executing machine learning models that analyze sensor data and determine optimal settings, transforming static user-configured parameters into dynamic optimized values
2Ease of operation
If asset settings are configured by users based on knowledge or written instructions, then ease of operation is improved, but energy consumption increases
Solution Approach 1:
The system enables self-service by allowing the asset to automatically optimize its own settings through sensor data collection and machine learning model execution, eliminating the need for manual user configuration while achieving optimal performance automatically
Solution Approach 2:
The system implements feedback by continuously collecting sensor data from the asset, executing machine learning models to analyze this data, and using the results to automatically adjust settings, creating a closed-loop system that reduces energy consumption through continuous optimization
3Ease of operation
If asset settings are configured by users based on knowledge or written instructions, then ease of operation is improved, but air quality deteriorates
Solution Approach 1:
The system enables self-service by allowing the asset to automatically optimize its own settings through sensor data collection and machine learning model execution, eliminating the need for manual user configuration while achieving optimal performance automatically
Solution Approach 2:
The system implements feedback by continuously collecting sensor data from the asset, executing machine learning models to analyze this data, and using the results to automatically adjust settings, creating a closed-loop system that reduces energy consumption through continuous optimization
Data Source
AI summary
Various embodiments described herein relate to providing asset behavior modeling. In this regard, a request to obtain one or more asset insights with respect to an asset is received. In response to the request, a model is executed based on sensor data associated with the asset and asset scenario data associated with different operation scenarios for the asset. The model represents predicted asset behavior of the asset to provide the one or more asset insights. Furthermore, in response to the request, one or more actions are performed based on the one or more asset insights.