Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances

TL;DR

This paper presents a novel deep learning model for multi-label classification in non-intrusive load monitoring that significantly reduces energy consumption while improving classification accuracy over existing methods.

Contribution

The paper introduces a new deep learning model for NILM that enhances energy efficiency and classification performance, along with an evaluation methodology for real-world scenario simulation.

Findings

Energy consumption reduced by over 23% compared to state-of-the-art.

Achieves approximately 8 percentage points better performance on benchmark datasets.

Outperforms random forest models by 12 percentage points in accuracy.

Abstract

Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand response applications and energy management systems as well as for awareness raising and motivation for improvements in energy efficiency. Recently, classical machine learning and deep learning (DL) techniques became very popular and proved as highly effective for NILM classification, but with the growing complexity these methods are faced with significant computational and energy demands during both their training and operation. In this paper, we introduce a novel DL model aimed at enhanced multi-label classification of NILM with improved computation and energy efficiency. We also propose an evaluation methodology for comparison of different models using data synthesized from the measurement datasets so as to better represent real-world scenarios. Compared to the state-of-the-art, the proposed model has its energy consumption reduced by more than 23% while providing on average approximately 8 percentage points in performance improvement when evaluating on data derived from REFIT and UK-DALE datasets. We also show a 12 percentage point performance advantage of the proposed DL based model over a random forest model and observe performance degradation with the increase of the number of devices in the household, namely with each additional 5 devices, the average performance degrades by approximately 7 percentage points.