Smart-Meter HVAC Inefficiency Prediction Before Summer Failure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Residential HVAC systems lack efficient methods for identifying maintenance needs, leading to energy waste and unnecessary expenditure, as they typically only detect inefficiencies when they fail to perform during summer weather, without self-test features available for residential applications.
Innovation Solution
A method using processors and machine learning algorithms to predict inefficient HVAC operation by obtaining and preprocessing data from households during spring and summer weather, calculating a Household Efficiency Score, and generating a classification model to identify inefficiencies based on power consumption patterns, HVAC properties, and temperature responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If HVAC systems use self-test output features to identify maintenance needs, then maintenance can be detected early, but this feature is only available on industrial systems and not residential systems
Solution Approach 1:
The patent introduces smart meters as intermediary devices that monitor electrical power consumption of HVAC systems. These smart meters act as mediators between the HVAC system and the monitoring system, enabling indirect detection of HVAC efficiency through power consumption patterns without requiring direct integration with HVAC self-test features.
Solution Approach 2:
The patent replaces the mechanical/electrical self-test output features with an electrical monitoring approach using smart meters. Instead of relying on HVAC system's own diagnostic outputs, the system substitutes this with external monitoring of electrical power consumption patterns to infer HVAC efficiency and maintenance needs.
2Difficulty of detecting and measuring
If HVAC inefficiency is only detected when the system fails to perform during summer weather, then detection is simple, but energy waste and costs increase due to late detection
Solution Approach 1:
The patent performs preliminary monitoring of HVAC power consumption patterns during spring weather conditions before the critical summer season arrives. By analyzing power consumption data and detecting efficiency degradation early in spring, the system enables proactive maintenance scheduling before inefficiency leads to significant energy waste during peak summer operation.
Solution Approach 2:
The patent establishes a feedback loop where smart meters continuously monitor HVAC power consumption and compare it against baseline patterns. When deviations indicating inefficiency are detected, the system generates alerts and recommendations for maintenance, creating a continuous feedback mechanism that enables timely intervention rather than waiting for complete system failure.
3Measurement precision
If machine learning models are trained on both spring and summer data, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the training data into distinct seasonal categories (spring weather data and summer weather data) and trains separate machine learning models for each season. This segmentation allows each model to specialize in detecting inefficiency patterns specific to its season, improving overall prediction accuracy while keeping individual model complexity manageable through focused training datasets.
Data Source
AI summary
A method for monitoring heating, ventilation, and air conditioning (HVAC) systems includes: Obtaining first training data for HVACs in a training set of households during a first period of spring weather; Obtaining second training data during a period of summer weather, Preprocessing the training data to identify repeating patterns of HVAC consumption or generating additional derived parameters, in an aggregation process; Calculating the amount of energy required to change house temperature; Applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of summer weather conditions; Obtaining operational data pertaining to HVACs in an operational set of households during a second period of spring weather; and Applying the HVAC classification model to predict inefficiency of HVACs at individual households in the operational set, during periods of summer weather using only overall household power consumption.