Comparison of non-intrusive load monitoring supervised methods using harmonics as feature

Al-Khadher, Omar; Mukhtaruddin, Azharudin; Hashim, Fakroul Ridzuan; Azizan, Muhammad Mokhzaini; Mamat, Hussin; Mani, Mohamed

doi:10.1109/icecet55527.2022.9872689

Cited by 2 publications

(1 citation statement)

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“…Year Place Method Used for NILM Dataset Avg. Efficiency (%) [15] China Particle swarm 1 year 94.2 [16] Indonesia Random Forests 1 year 99 [17] India Markov Chain 31 days 94 [5] Estonia Extreme Gradient Boost (XgBoost) 3 years 97.2 [18] Malaysia K-NN, SVM, Ensemble 30 days 98.8 [19] Iran SVM 1 week 98.2 [20] Indonesia Convolutional Neural Networks (CNN) 1 month 98 [21] Italy Random Forests 27 months 96.3 [22] Spain Long Short-Term Memory Networks (LSTM) 7 months 98 [23] Greece Recurrent Neural network (RNN) 10 days 97 [24] Canada LSTM 2 days 98 [ The NILM method has been used for anomaly detection at the appliance level by incorporating machine learning [26]. In another study [8], NILM is utilized for the event matching of devices.…”

Section: Studymentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Shabbir,

Vassiljeva,

Nourollahi Hokmabad

et al. 2024

Electronics

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Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

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