Non-intrusive load transient identification based on multivariate LSTM neural network and time series data augmentation. (September 2021)

• Record Type: Journal Article
• Authors: Wu, Haiping; Liu, Hui
• Is Part Of: Sustainable energy, grids and networks. Volume 27(2021)
• Publication Date: 2021-09
• Subjects: Non-intrusive load monitoring -- Load transient identification -- Time series data augmentation -- Deep learning -- LSTM neural network; Renewable energy sources -- Periodicals; Smart power grids -- Periodicals; Electric power systems -- Periodicals; 333.794
• DOI: 10.1016/j.segan.2021.100490
• Languages: English
• ISSNs: 2352-4677

Abstract

Abstract: Getting the specific energy consumption information of each appliance is an effective way to reduce energy wastage and improve energy efficiency. The non-intrusive load monitoring is a promising solution to achieve this goal. In terms of the event-based non-intrusive load monitoring, the load transient identification models have attracted great attention recently. In this study, a hybrid load transient identification model based on multivariate fusion, time series data augmentation, and deep neural network computation is proposed. In the hybrid model, the multivariate LSTM neural network is utilized to extend the traditional template matching methods to the deep learning field, which is beneficial to improve the identification accuracy and the efficiency in the testing stage. A hybrid time series data augmentation framework is designed to handle the class-imbalance and insufficient sample size, which is often the case in non-intrusive load monitoring. The effectiveness of each component, as well as the whole hybrid model, are evaluated based on the BLUED dataset. The comparison results indicate that (a) multivariate LSTM neural network is effective in modeling the characters of the load transient; (b) the proposed hybrid time series data augmentation framework is effective in improving the overall performance of the multivariate LSTM neural network; (c) taking identification accuracy and the application efficiency in testing stage into consideration, the proposed hybrid model performs better than that of the state of the art Dynamic Time Warping (DTW) based models.