Building management system with multi-dimensional analysis of building energy and equipment performance
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Solution Overview
Problem
Current building management systems (BMS) lack effective tools for comprehensive data analysis and visualization, making it difficult to compare energy consumption and equipment performance across different buildings and equipment categories, leading to inaccurate benchmarking and fault detection.
Innovation Solution
A BMS that includes a metrics engine to calculate and visualize performance metrics across various dimensions, such as energy consumption and fault metrics, using graphical representations like rectangles with scaled length, breadth, and color to facilitate size-based and color-based comparisons, allowing for normalized values and percentage deviations across equipment categories and buildings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional BMS data analysis methods are used, then the system is simple to operate, but the measurement precision and accuracy of benchmarking are insufficient
Solution Approach 1:
The patent introduces multi-dimensional analysis by displaying data across three dimensions: building identity, equipment category, and time period. This dimensional transformation enables comprehensive benchmarking by allowing users to compare energy consumption and fault metrics across different buildings, equipment types, and timeframes simultaneously, thereby improving measurement precision without overwhelming complexity
Solution Approach 2:
The patent employs an intermediary processing layer that collects raw data from multiple buildings and equipment, normalizes it according to predefined metrics, and presents it in a standardized format. This intermediary layer handles the complexity of data aggregation and normalization, allowing users to access precise comparative data without directly managing the underlying system complexity
2Measurement precision
If comprehensive data collection from multiple buildings is implemented, then the benchmarking accuracy improves, but the loss of information and data management complexity increases
Solution Approach 1:
The patent transforms raw data into normalized metrics by applying parameter changes. Energy consumption data is normalized by building area and time period, fault data is aggregated by equipment category and time frame. These parameter transformations enable accurate comparison across diverse data sources while maintaining data integrity and preventing information loss through systematic normalization
3Reliability
If detailed equipment performance tracking is implemented, then fault detection accuracy improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments fault detection by dividing equipment into distinct categories (HVAC, lighting, electrical, etc.) and tracking faults within each category separately. This segmentation allows detailed monitoring of specific equipment types while organizing data in a manageable structure, improving fault detection accuracy without creating an unmanageably complex system
4Ease of operation
If multi-dimensional visualization is implemented, then the ease of operation and data interpretation improves, but the device complexity increases
Solution Approach 1:
The patent creates a universal visualization interface that handles multiple functions: displaying energy consumption data, fault metrics, and comparative analysis across buildings and equipment categories. This single multi-functional interface consolidates what would otherwise require multiple separate tools, improving ease of operation while managing system complexity through integration
Data Source
AI summary
A building management system includes building equipment, a metrics engine, and a visualization module. The building equipment is configured to provide raw data samples of one or more data points. The metrics engine is configured to collect the raw data samples, and calculate a first metric, a second metric, and a third metric as a function of the raw data samples. The visualization module is configured to correlate the first metric to a first dimension value, the second metric to a second dimension value, and the third metric to a third dimension value. The visualization module is also configured to generate a graphical visualization in which the first dimension value is displayed as a first dimension of the graphical visualization, the second dimension value is displayed as a second dimension of the graphical visualization, and the third dimension value is displayed as a third dimension of the graphical visualization.