RNN Home Appliance Classification Model Using Power Data
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Solution Overview
Problem
Conventional home appliance classification models based on hidden Markov models face challenges with increasing complexity and accuracy issues as the number of appliances grows, and struggle with multi-state power consumption modeling, leading to low classification accuracy.
Innovation Solution
The implementation of a recurrent neural network (RNN)-enhanced model for deep learning, which learns sequential data patterns and uses a data structure designed for high-level learning, along with a low pass filter to encode power consumption variations, to create a more accurate home appliance classification model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If HMM-based models are used for home appliance classification, then the model can be implemented with conventional methods, but classification accuracy decreases as the number of home appliances increases
Solution Approach 1:
The patent replaces the conventional HMM-based classification system with a deep learning system comprising a power consumption data acquisition module, data preprocessing module, and classification model module. The classification model uses neural networks with multiple layers (input layer, hidden layers, and output layer) to automatically learn features from power consumption data, substituting the manual feature engineering and probability-based HMM approach with an automated data-driven approach that maintains accuracy as appliance count increases.
Solution Approach 2:
The patent introduces a multi-dimensional approach by collecting not only power consumption data but also operation time, on/off status, and other operational parameters of appliances. The deep learning model processes these multi-dimensional features simultaneously across multiple hidden layers, enabling the system to distinguish between more appliances without increasing model complexity in the traditional HMM sense.
2Measurement precision
If HMM-based models are used for single-state power consumption, then accurate modeling is achieved, but classification accuracy decreases when multi-state power consumption is modeled
Solution Approach 1:
The patent implements a dynamic classification approach where the deep learning model learns to handle multiple power consumption states (multi-state) of appliances. The neural network's hidden layers dynamically adjust weights and biases based on the input power consumption patterns, enabling accurate classification whether appliances operate in single-state or multi-state modes. The model adapts to different operational states through training on diverse power consumption data.
Solution Approach 2:
The patent changes the parameters being analyzed by incorporating multiple operational parameters (power consumption, operation time, on/off status) rather than relying solely on power consumption values. This multi-parameter approach enables the deep learning model to distinguish between different operational states of appliances, maintaining high classification accuracy regardless of whether appliances exhibit single-state or multi-state power consumption patterns.
3Adaptability or versatility
If more home appliances are classified using conventional models, then comprehensive monitoring is achieved, but model complexity increases and accuracy decreases
Solution Approach 1:
The patent creates a universal classification system using deep learning that can handle any number of appliances through a single unified model architecture. The neural network is designed with flexible input and output layers that can accommodate varying numbers of appliances without requiring separate models or significant model modifications. This universal approach maintains high classification accuracy whether monitoring a few or many appliances simultaneously.
Solution Approach 2:
The patent segments the classification task into distinct functional modules: data acquisition module that collects power consumption data, data preprocessing module that cleans and prepares the data, and classification model module that performs the actual classification. This segmentation allows each module to be optimized independently and enables the system to scale to handle more appliances without increasing overall model complexity, as the modular architecture processes additional appliances through the same streamlined pipeline.
Data Source
AI summary
Provided are an apparatus and method for classifying home appliances based on power consumption using deep learning, which can efficiently classify home appliances in use by applying deep learning and analyzing power data collected from a house. The apparatus includes a home appliance classification model creation module configured to encode power consumption data collected from a house to learn a home appliance classification model and create an RNN-based home appliance classification model and a home appliance classification module configured to collect and encode data on power consumption currently in use and classify home appliances using the home appliance classification model created by the home appliance classification model creation module.