A Multi-Agent NILM Architecture for Event Detection and Load Classification

Abstract

A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

Scattering Transform for Classification in Non-Intrusive Load Monitoring

“…Nevertheless, there is no disaggregation since the CNN input is obtained from a submetering network. In [7], a multiagent strategy was proposed to improve NILM classification, achieving accuracy results above 95% on LIT-dataset. Although this result was superior to the related literature, applying the multiagent strategy in realistic cases may be compromised due to the high computational complexity.…”

“…In this second case, we train models with single and three loads and evaluate the model's classification performance with two, three, and eight loads. Therefore, it is possible to analyze the classifier's generalization, as proposed in [7]. Figure 1 shows the two approaches, detailed as follows.…”

“…We train five different classification methods, typically used in NILM approaches [7]: K-Nearest Neighbors (k-NN), Ensemble Method (ENS), Support Vector Machines (SVMs), Decision Trees (DTs), and Linear Discriminant Analysis (LDA), briefly detailed as follows:…”

“…This method fully relies on the distances between test samples and the training set, and due to that, can be considered one of the simplest classification approaches [46]. However, it requires the storage of all training examples, which may be a limitation for embedded systems with more restrictive memory requirements [7]. • Decision Tree: this method employs several concatenated binary splits arranged in a tree structure.…”

“…In the event-based, on the other hand, load transitions are detected, and, subsequently, disaggregation is performed. Generally, NILM classification methods based on events are developed in four basic steps [7]: (i) voltage and current acquisition; (ii) event detection; (iii) extraction of relevant information (features); and (iv) appliance classification.…”