Power Metering Sensor with Cloud Pattern Analysis
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Solution Overview
Problem
Conventional energy consumption measurement systems lack the granularity and real-time data needed to effectively monitor and control energy usage within buildings, making it difficult to manage energy efficiency and adherence to energy targets.
Innovation Solution
A power consumption metering system that uses sensors to measure granular-level energy consumption patterns, processes this data with a cloud-based system to identify the status and operation time of energy consumers, allowing for detailed analysis and control without requiring additional hardware or software on the consumers themselves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional energy consumption measurement at central supply point is used, then billing purposes are satisfied, but energy efficiency control and real-time monitoring capability are insufficient
Solution Approach 1:
The patent introduces a current transformer as an intermediary device that indirectly measures power consumption through current sensing. This mediator enables granular-level measurement without direct integration into the power consumer, resolving the contradiction by providing detailed data while maintaining system simplicity.
Solution Approach 2:
The patent replaces complex hardware integration with a cloud-based data processing system that analyzes current patterns. This substitution of mechanical/electrical complexity with computational processing enables detailed monitoring while keeping the physical metering system simple.
2Measurement precision
If granular-level power consumption measurement is implemented, then energy efficiency control is improved, but system complexity increases
Solution Approach 1:
The current transformer serves as a simple intermediary that provides precise current measurement without requiring complex integration. This mediator enables high measurement precision while keeping the device complexity low, as it is a standalone component that interfaces with existing electrical infrastructure.
Solution Approach 2:
The cloud-based data processing system performs automatic pattern recognition and status identification without requiring complex local hardware. The system serves itself by using computational algorithms to extract meaningful information from raw current data, achieving high measurement precision through software rather than hardware complexity.
3Loss of information
If operation time counting is added to the metering system, then energy target monitoring is improved, but device complexity increases
Solution Approach 1:
The system automatically counts operation time by analyzing current patterns and identifying operational status through cloud-based processing. This self-service approach extracts operation time information from existing measurement data without requiring separate counters or additional hardware, thus improving information availability while maintaining system simplicity.
Solution Approach 2:
The cloud-based data processing system performs multiple functions including power consumption measurement, pattern recognition, status identification, and operation time counting. This multi-functionality allows the system to provide operation time information without adding dedicated components, as the same processing infrastructure serves multiple purposes.
Data Source
AI summary
In an embodiment a method for operating a power consumption metering system includes measuring, by a sensor deployed at a monitored site, power consumption values over time to obtain a high speed value pattern of a power consumption with a resolution of more than 1000 values per second, measuring, by the sensor, low speed power consumption values over time to obtain a low speed value pattern of the power consumption with a resolution of less than 100 values per second, identifying a status of a power consumer of the monitored site dependent on the high speed value pattern and counting an operation time of the power consumer dependent on the low speed value pattern and on the identified status.