Method and system for HVAC malfunction and inefficiency detection over smart meters data
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Solution Overview
Problem
Existing systems fail to automatically detect and analyze HVAC system malfunctions or inefficiencies in households without direct power consumption measurement, leading to delayed servicing of high power-consuming appliances.
Innovation Solution
A method and system utilizing processors and machine learning algorithms to monitor and analyze household power consumption, environmental conditions, and HVAC-specific data to extract weighted failure indications, determine malfunction probabilities, and emit alerts on HVAC malfunction types, probable causes, and suggested actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct power consumption measurement sensors are installed for each HVAC system, then measurement precision is improved, but device complexity and installation cost increase
Solution Approach 1:
The patent uses smart meters as intermediary devices to measure overall household power consumption and environmental conditions, then applies machine learning algorithms to indirectly derive HVAC power consumption without installing dedicated sensors on each HVAC unit. This mediator approach resolves the contradiction by achieving measurement precision through computational inference rather than direct physical measurement.
Solution Approach 2:
The patent replaces the mechanical/physical measurement system (direct power consumption sensors on HVAC equipment) with an information-processing system (smart meters collecting electrical data and machine learning algorithms analyzing patterns). This substitution eliminates the need for complex physical sensor installations while maintaining measurement capability through data analysis.
2Productivity
If manual monitoring and analysis of power consumption data is performed, then ease of operation is maintained, but productivity and detection timeliness deteriorate
Solution Approach 1:
The system performs self-service by automatically collecting data from smart meters, processing the data through machine learning algorithms, detecting HVAC malfunctions, and generating alerts without human intervention. This automation dramatically improves detection speed and productivity while the standardized algorithmic approach keeps operational complexity manageable.
Solution Approach 2:
The system implements continuous feedback loops where power consumption data is constantly monitored, analyzed against learned patterns, and used to generate real-time alerts about HVAC conditions. This automated feedback mechanism enables rapid detection and response to malfunctions, improving productivity without requiring manual analysis efforts.
3Reliability
If real-time data aggregation and analysis is implemented across multiple households, then reliability of detection is improved, but loss of information and data processing complexity increase
Solution Approach 1:
The patent segments the large-scale data processing task by having each household's smart meter independently collect and pre-process its own data locally, then transmit only relevant aggregated results to the central analysis system. This segmentation reduces data transmission requirements and manages information complexity while maintaining detection reliability through distributed data collection.
Data Source
AI summary
The present invention provides a method for determining conditions of malfunction or inefficiency of HVAC systems, within a plurality of monitored households, in which there are no sensors for directly measuring the power consumption per specific HVAC. The said method comprise the steps of: a. monitoring the power consumption of a plurality of households; b. monitoring the concurrent environmental conditions at the location of the said plurality of households; c. analyzing each household's power consumption, and extracting weighted failure indications of inefficient or malfunctioning HVAC systems; d. determining the probability of various HVAC conditions of malfunction of inefficiency, according to the said weighted indicators; and e. emitting a an alert in relation to the said condition of HVAC malfunction of inefficiency, comprising at least one of: HVAC malfunction type, probability, probable cause, and suggested action.