Abstract
Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of the energy consumption and for future smart grid applications. Here, we are interested in an event-based NILM pipeline, and particularly in the HEAs’ recognition step. This paper focuses on the selection of relevant and understandable features for efficiently discriminating distinct HEAs. Our contributions are manifold. First, we introduce a new publicly available annotated dataset of individual HEAs described by a large set of electrical features computed from current and voltage measurements in steady-state conditions. Second, we investigate through a comparative evaluation a large number of new methods resulting from the combination of different feature selection techniques with several classification algorithms. To this end, we also investigate an original feature selection method based on a deep neural network architecture. Then, through a machine learning framework, we study the benefits of these methods for improving Home Electrical Appliance (HEA) identification in a supervised classification scenario. Finally, we introduce new transfer learning results, which confirm the relevance and the robustness of the selected features learned from our proposed dataset when they are transferred to a larger dataset. As a result, the best investigated methods outperform the previous state-of-the-art results and reach a maximum recognition accuracy above 99% on the PLAID evaluation dataset.
Highlights
During the last decades, the electricity consumption in the residential sector increased steadily with the worldwide population growth and became a major ecological issue
Method are diversified in terms of harmonic orders; For both datasets, the features selected by the Mutual Information (MI) and Linear Discriminant Analysis (LDA) methods are related to odd-order harmonics, which describe the power supply structures included in most of the Home Electrical Appliances (HEAs); For the sequential forward Feature Selection (FS) method, our experiments compare the results provided by the Euclidean-based KNN classifier and to the LDA classifier
As several common features are selected from each dataset separately, we assume that they convey common information that can be transferred from one dataset to another
Summary
The electricity consumption in the residential sector increased steadily with the worldwide population growth and became a major ecological issue. The main advantages are the control and the understanding of their electricity consumption through a transparent access and a promptly forwarded information. It can improve the load-forecasting accuracy and provide a basic scheme to set up energy management strategies [2]. In this context, Non Intrusive Load Monitoring (NILM) methods offer an efficient answer, since they provide a breakdown of the residential energy consumption without instrumenting each HEA. Relevant features that meet the additive criterion [8] (which is required for the subsequent steps) are computed to recognize the HEA electrical signature that triggered an event using a pattern matching method
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