Power Profile and Thresholding Assisted Multi-Label NILM Classification

Abstract

Next-generation power systems aim at optimizing the energy consumption of household appliances by utilising computationally intelligent techniques, referred to as load monitoring. Non-intrusive load monitoring (NILM) is considered to be one of the most cost-effective methods for load classification. The objective is to segregate the energy consumption of individual appliances from their aggregated energy consumption. The extracted energy consumption of individual devices can then be used to achieve demand-side management and energy saving through optimal load management strategies. Machine learning (ML) has been popularly used to solve many complex problems including NILM. With the availability of the energy consumption datasets, various ML algorithms have been effectively trained and tested. However, most of the current methodologies for NILM employ neural networks only for a limited operational output level of appliances and their combinations (i.e., only for a small number of classes). On the contrary, this work depicts a more practical scenario where over a hundred different combinations were considered and labelled for the training and testing of various machine learning algorithms. Moreover, two novel concepts—i.e., thresholding/occurrence per million (OPM) along with power windowing—were utilised, which significantly improved the performance of the trained algorithms. All the trained algorithms were thoroughly evaluated using various performance parameters. The results shown demonstrate the effectiveness of thresholding and OPM concepts in classifying concurrently operating appliances using ML.

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

“…Classic ML algorithms utilized in reviewed work are Random k-labELset (RAkEL) [11], [29], [34], factorial Hidden Markov Model (fHMM) [26], Random Forrest (RF) [11], [35], Sparse Representation based Classification (SRC) [30], Classification And Regression Tree (CART) [35], Extra Tree (ET) [35], k-Nearest Neighbors (kNN) [11], [35], Linear Discrimination Analysis (LDA) [35] and Naïve Bayes (NB) [35].…”

“…Their findings indicate that Random Forest (RF) outperforms other learning algorithms. Similarly, Rehmani et al [35] demonstrated that computationally intensive deep learning (DL) algorithms, such as CNN and RNN, were not required for their particular datasets, as already classical machine learning algorithms, such as kNN and RF, yielded accuracy of 99 %. However, openly available and well documented REFIT and UK-DALE datasets, recently used in many reference works as well as in this study, do not exhibit suitable performance with the classical machine learning and are used with DL models.…”