Sequence to Point Learning Based on an Attention Neural Network for Nonintrusive Load Decomposition

Abstract

Nonintrusive load monitoring (NILM) analyzes only the main circuit load information with an algorithm to decompose the load, which is an important way to help reduce energy usage. Recent research shows that deep learning has become popular for this problem. However, the ability of a neural network to extract load features depends on its structure. Therefore, more research is required to determine the best network architecture. This study proposed two deep neural networks based on the attention mechanism to improve the current sequence to point (s2p) learning model. The first model employs Bahdanau style attention and RNN layers, and the second model replaces the RNN layer with a self-attention layer. The two models are both based on a time embedding layer. Therefore, they can be better applied in NILM. To verify the effectiveness of the algorithms, we selected two open datasets and compared them with the original s2p model. The results show that attention mechanisms can effectively improve the model’s performance.

Transfer learning for multi-objective non-intrusive load monitoring in smart building

“…In a seq2point architecture, the raw data is divided into short time windows (i.e., sequences), and the data from each window of the main electricity meter is used to estimate the power consumption of the targeted appliance at the window's midpoint using a neural network [39]. The model can be expressed as…”

Smart Processing for Systems under Uncertainty or Perturbation

“…Research on algorithm implementation was carried out in [1][2][3]. Yang et al propose two deep neural networks based on the attention mechanism to improve a sequence-topoint learning model.…”

Application of attention mechanism enhanced neural network in non-invasive load monitoring of industrial power data

“…The NILM methods can further obtain electrical information, which can help reduce power consumption costs and enable a more rational allocation of power resources. [7][8][9] Although most NILM research focuses on household energy use due to the difficulty of obtaining data, applying NILM to the industry has gradually gained wide attention in recent years. [10][11][12][13][14] In recent years, there is growing use of computeraided models (e.g.…”