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

Abstract

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.

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

“…However, these models often require extensive computational resources, especially when handling large datasets or performing multi-step forecasting. For instance, SVMs can become computationally intensive with large training sets due to their quadratic optimization problem, while ensemble methods such as Random Forests involve training multiple decision trees, which increases computational time linearly with the number of trees and depth [75]. Considering the computational effort estimation in an LSTM network as an example, the time and energy for model training depend on both hardware and software, and the total number of model parameters serves as a useful surrogate.…”