Data Quality Assessment System for Building Sensor Networks
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Solution Overview
Problem
Existing systems for determining the quality level of performance data from building management systems and sensors are limited, often providing inaccurate data due to faulty sensors, calibration issues, and environmental factors, which can lead to incorrect decision-making and increased costs.
Innovation Solution
A method and system for determining the quality level of performance data using a Data Quality Assessment System (DQAS) that analyzes data from multiple sources, generates events based on quality indicators, classifies these events, and assigns a quality index value to determine the overall quality level of the performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional quality indicators are used to determine data quality, then individual sensor level data can be evaluated, but the analysis remains limited to trend analysis and does not map with the domain from which data is collected
Solution Approach 1:
The system segments data quality evaluation into multiple dimensions: data completeness, data accuracy, and domain-level relevance. Each dimension is evaluated separately using specialized indicators, then aggregated to provide comprehensive domain-level quality assessment that maps to business domains rather than just sensor-level metrics
Solution Approach 2:
The patent transitions from one-dimensional sensor-level quality indicators to multi-dimensional quality evaluation that includes domain context. By adding the dimension of domain mapping and business relevance, the system achieves both precise measurement and versatile application across different business domains
2Productivity
If sensors are deployed in building management systems to collect performance data, then energy usage patterns can be monitored, but data accuracy deteriorates due to faulty sensors, calibration issues, and environmental factors
Solution Approach 1:
The system continuously monitors data quality metrics and provides feedback to identify and isolate faulty sensors. By comparing actual sensor readings against expected ranges and historical patterns, the system detects calibration drift and environmental interference, then adjusts or flags affected data to maintain analysis reliability
Solution Approach 2:
The patent implements preventive measures by establishing baseline calibration values and tolerance ranges before deployment. The system proactively adjusts for known environmental factors like radiation effects on temperature sensors and appliance interference, cushioning against potential data corruption before it impacts analysis
3Quantity of substance
If low quality data is included in analysis, then more comprehensive data coverage is achieved, but decision-making accuracy deteriorates due to incorrect or unreliable data
Solution Approach 1:
The system applies different quality thresholds and evaluation criteria to different data sources and domains. Rather than uniformly accepting or rejecting all data, it locally assesses quality metrics for each sensor and domain context, accepting high-quality data while filtering or flagging low-quality data in a targeted manner that preserves overall data coverage without compromising decision accuracy
Data Source
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AI summary
The present disclosure comprises a method for determining quality level of performance data associated with an entity group comprising one or more entities. The method comprises analyzing, by a data quality assessment system, performance data, received from one or more data sources of the entity group, using one or more quality evaluation parameters associated with the performance data. Thereafter, the data quality assessment system generates one or more events based on the analysis of the performance data. The one or more events are classified by the data quality assessment system, based on predefined event processing guidelines. Further, the data quality assessment system determines a quality index value for each of the one or more entities based on the one or more classified events, using the predefined event processing guidelines. Thereafter, the data quality assessment system determines the quality level of the performance data associated with the entity group.