Non-Intrusive Load Classification and Recognition Using Soft-Voting Ensemble Learning Algorithm With Decision Tree, K-Nearest Neighbor Algorithm and Multilayer Perceptron

Abstract

Non-intrusive load monitoring (NILM) detects the energy consumption of individual appliances by monitoring the overall electricity usage in a building. By analyzing voltage and current characteristics, NILM can recognize the usage patterns of various appliances, thus facilitating energy conservation and management. To implement non-intrusive load classification and recognition more effectively, this study proposes an ensemble learning algorithm based on soft voting, which comprises a decision tree, K-nearest neighbor algorithm, and multilayer perceptron (EL-SVDT-KNN-MLP). In this study, the voltage and current features in the plug-load appliance identification dataset (PLAID) and worldwide household and industry transient energy dataset (WHITED) are used as input data. The dataset is examined thoroughly and preprocessed before it is fed into the EL-SVDT-KNN-MLP. During preprocessing, six different normalization techniques are applied to the data to improve the accuracy and reliability of the machinelearning model, thus rendering the proposed algorithm more adept at classifying and recognizing appliances. The proposed method is validated by comparing it with other machine learning algorithms in terms of accuracy, precision, recall, and F1 score under the six different normalization methods. The results show that the proposed EL-SVDT-KNN-MLP algorithm outperforms the other ten machine learning algorithms examined in this study.

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

“…The classification of an object is based on the most frequently occurring class among the identified k nearest points. The parameter k denotes the number of nearest neighbors influencing the classification process, and the selection of an appropriate k is a nuanced yet crucial step for optimizing the model's performance [43].…”

“…Essentially, the synergy between DTW and K-NN capitalizes on DTW's efficacy in capturing temporal nuances and K-NN's proficiency in pattern classification based on similarity. This combined approach facilitates a more comprehensive analysis of time series data, proving particularly beneficial when dealing with complex and dynamic patterns [43].…”

“…Although metrics like mean squared error (MSE) and false positive/negative rates offer insights into overall accuracy, the evaluation should extend to specific appliance identification metrics such as precision, recall, and F1-score [22]. These metrics provide a granular understanding of how well the system distinguishes between individual appliances, which is essential for practical implementation in real-world scenarios [10,43]. It is essential not to overlook the need for additional signal processing when integrating neural networks (NNs) into applications.…”