A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks

TL;DR

This paper introduces a hybrid deep learning model combining LSTM and CNN for non-intrusive load monitoring, significantly improving accuracy in appliance-level energy disaggregation using real-world data.

Contribution

The paper presents a novel hybrid LSTM-CNN approach for NILM, leveraging sequence-to-sequence learning to enhance disaggregation accuracy over existing methods.

Findings

Achieved 95.93% accuracy improvement

F1-score increased by 80.93%

Demonstrated superior performance on REFIT dataset

Abstract

Energy disaggregation or nonintrusive load monitoring (NILM), is a single-input blind source discrimination problem, aims to interpret the mains user electricity consumption into appliance level measurement. This article presents a new approach for power disaggregation by using a deep recurrent long short term memory (LSTM) network combined with convolutional neural networks (CNN). Deep neural networks have been shown to be a significant way for these types of problems because of their complexity and huge number of trainable paramters. Hybrid method that proposed in the article could significantly increase the overall accuracy of NILM because it benefits from both network advantages. The proposed method used sequence-to-sequence learning, where the input is a window of the mains and the output is a window of the target appliance. The proposed deep neural network approach has been applied to real-world household energy dataset "REFIT". The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses around the UK. The proposed method achieve significant performance, improving accuracy and F1-score measures by 95.93% and 80.93% ,respectively which demonstrates the effectiveness and superiority of the proposed approach for home energy monitoring. Comparison of proposed method and other recently published method has been presented and discussed based on accuracy, number of considered appliances and size of the deep neural network trainable parameters. The proposed method shows remarkable performance compare to other previous methods.