Edge Facility Analytics for Interpretable Building Data
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Solution Overview
Problem
Existing facility analytics systems struggle to effectively gather, interpret, and utilize data from building automation systems, leading to inefficiencies and missed opportunities for energy savings and cost reduction.
Innovation Solution
A computer-based method and system that tags building automation system points with unique identifiers and semantic labels, using machine learning and real-time analytics to analyze dynamic data, providing real-time reporting, predictive maintenance, and customizable dashboards for energy management and cost optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from facility systems is gathered through existing techniques, then data collection is achieved, but the data is difficult to gather and interpret
Solution Approach 1:
The patent introduces an intermediary processing layer that sits between data collection devices and users. This layer automatically tags, categorizes, and contextualizes facility data, transforming raw information into interpretable insights without requiring users to directly handle complex data gathering processes.
Solution Approach 2:
The system enables self-service data interpretation through automated tagging and classification mechanisms. Data points automatically annotate themselves with relevant metadata and categories, eliminating the need for manual data processing and making information readily interpretable upon collection.
2Loss of energy
If existing facility analytics systems are used, then basic data collection is possible, but energy savings and cost reduction opportunities are missed
Solution Approach 1:
The system performs preliminary analysis and tagging of facility data in real-time, identifying energy savings opportunities and cost reduction potentials before decisions are made. This advance preparation enables proactive optimization rather than reactive problem-solving.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor facility operations and provide actionable insights for energy optimization. The system learns from patterns in the data and provides recommendations that directly address energy waste and cost inefficiencies.
3Loss of information
If more facility data is collected and analyzed, then better insights are achieved, but system complexity increases
Solution Approach 1:
The patent segments facility data into distinct categories and types, applying specific tagging rules and analysis methods to each segment. This modular approach allows comprehensive data analysis while maintaining manageable system complexity through organized, modular processing.
Solution Approach 2:
The system employs universal tagging standards and classification frameworks that can handle multiple types of facility data through a single integrated approach. This multi-functional tagging system reduces complexity by providing a unified method for processing diverse data sources.
Data Source
AI summary
A network edge device includes a local network interface, a remote network interface, a memory and a processor. The local network interface receives data from local data sources over a local network. The remote network interface communicates with a remote computer over a remote network. The processor receives a filter parameter set for each local data source from the remote computer. Each filter parameter set includes a data rate parameter and at least one of a minimum threshold parameter, a maximum threshold parameter or a change-of-value (COV) parameter. For each local data source, the processor compare each data value received from the local data source to at least one of the filter parameters, and sends the data value and subsequent data values to the remote computer at a data rate that is based on the data rate parameter.