An End-to-End Machine Learning based Unified Architecture for Non-Intrusive Load Monitoring

S Wali,M H U Haq,M Kazmi,S A Qazi

doi:10.48084/etasr.4142

Abstract

Non-Intrusive Load Monitoring (NILM) or load disaggregation aims to analyze power consumption by decomposing the energy measured at the aggregate level into constituent appliances level. The conventional load disaggregation framework consists of signal processing and machine learning-based pipelined architectures, respectively for explicit feature extraction and decision making. Manual feature selection in such load disaggregation frameworks leads to biased decisions that eventually reduce system performance. This paper presents an efficient End-to-End (E2E) approach-based unified architecture using Gated Recurrent Units (GRU) for NILM. The proposed approach eliminates explicit feature engineering and has a unified classification and prediction model for appliance power. This eventually reduces the computational cost and enhances response time. The performance of the proposed system is compared with conventional algorithms' with the use of recall, precision, accuracy, F1 score, the relative error in total energy and Mean Absolute Error (MAE). These evaluation metrics are calculated on the power consumption of top priority appliances of Reference Energy Disaggregation Dataset (REDD). The proposed architecture with an overall accuracy of 91.2 and MAE of 25.23 outperforms conventional methods for all electrical appliances. It has been showcased through a series of experiments that feature extraction and event-based approaches for NILM can readily be replaced with E2E deep learning techniques allowing simpler and cost-efficient implementation pathways.

Highlights

Energy demand is increasing drastically with the increase in industrial development
Since this load disaggregation approach does not depend on several data recorder sensors, it is a costeffective solution adopted for demand reduction and load forecasting
In order to address all of the above limitations in previously proposed Non-Intrusive Load Monitoring (NILM) approaches, this paper presents an efficient end-to-end (E2E) Machine Learning (ML) based unified architecture using Gated Recurrent Units (GRU)

Summary

INTRODUCTION

Energy demand is increasing drastically with the increase in industrial development. This raises the need of managing energy usage effectively at consumer end. The basic idea of NILM revolves around the decomposition of total demand into appliance level power consumption Since this load disaggregation approach does not depend on several data recorder sensors, it is a costeffective solution adopted for demand reduction and load forecasting. The second category, i.e. the event-less approach, does not rely on event detection It primarily uses statistical and probability-models to match consumption signal of single or group appliances to the aggregate power signal [3]. If the same sequence predicts power at specific time instant only it is termed as sequence-to-point Both sequence-to-point and sequence-to-sequence NILM approaches [15] were based on CNN architecture. One of the major drawbacks in the above stated deep learning based NILM architectures is their dependency on explicit features extraction from signal. The performance edge of the proposed approach is showcased on REDD, which is a renowned load disaggregation dataset

THE PROPOSED E2E MACHINE LEARNING BASED UNIFIED

Load Disaggregation Dataset and Preprocessing

RESULTS AND COMPARISON

CONCLUSION