HVAC Timeseries Dimensional Mismatch Handling Using DCT Transformation
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Solution Overview
Problem
Building HVAC systems face challenges in automatically handling dimensional mismatches between sensors and actuation devices, leading to errors in data comparison and analysis due to differences in sampling rates and units of measurement, which complicates fault detection, benchmarking, and compliance reporting.
Innovation Solution
The implementation of a HVAC system that uses a controller to apply discrete cosine transformations (DCT) to actuation signal and sensor response timeseries, identifies similarities, and corrects dimensional mismatches by modifying timeseries to ensure equal sample numbers or applying quantization to DCT coefficients, allowing for accurate comparison and device pairing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If timeseries data from sensors and actuation devices are collected for analysis, then data availability and analytical capability are improved, but dimensional mismatches due to different sampling rates and units of measurement cause errors in data comparison
Solution Approach 1:
The patent applies discrete cosine transformation (DCT) to convert timeseries data from time-domain to frequency-domain representation. This parameter transformation allows comparison of data with different sampling rates by converting them to a common frequency-domain space where dimensional mismatches are resolved, enabling accurate data comparison while preserving complete information from all sensors and actuation devices
2Reliability
If manual identification of associations between sensors and actuation devices is performed, then relationship accuracy can be ensured, but the process becomes cumbersome, error-prone, and time-consuming
Solution Approach 1:
The system automatically identifies associations between sensors and actuation devices by analyzing the transformed timeseries data and determining correlations without human intervention. The controller performs self-service by autonomously pairing devices based on data patterns, eliminating the need for manual configuration while maintaining high pairing accuracy through mathematical analysis of the timeseries relationships
3Ease of operation
If timeseries data with different sampling rates are compared directly, then processing simplicity is maintained, but dimensional mismatches prevent accurate comparison and analysis
Solution Approach 1:
The discrete cosine transformation serves as an intermediary process that mediates between timeseries data with different sampling rates. By transforming all data into the frequency-domain through DCT, the system creates a common intermediate representation that eliminates dimensional mismatches while maintaining the simplicity of the comparison operation, as the transformation handles the complexity of rate synchronization automatically
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables automatic device pairing and corrects dimensional mismatches, facilitating accurate data comparison and analysis, improving fault detection, benchmarking, and compliance reporting in HVAC systems.
Implementation Method 1
The controller is configured to apply a discrete cosine transformation (DCT) to each actuation signal timeseries and each sensor response timeseries. Each DCT generates a set of DCT coefficients.
Data Source
AI summary
A heating, ventilation, and air conditioning (HVAC) system for a building includes a plurality of actuation devices, a plurality of sensors, and a controller. The actuation devices receive actuation signals and operate in accordance with the actuation signals to affect one or more variables in the building. The sensors measure the variables affected by the actuation devices and provide sensor response signals including values of the measured variables. The controller generates actuation signal timeseries including samples of the actuation signals and sensor response timeseries including samples of the sensor response signals. The controller applies a discrete cosine transformation (DCT) to each actuation signal timeseries and each sensor response timeseries. Each DCT generates a set of DCT coefficients. The controller identifies a similarity between two or more of the actuation signal timeseries and the sensor response timeseries by comparing the DCT coefficients resulting from the DCTs.