Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network

Abstract

Load decomposition technology is an important aspect of power intelligence. At present, there are mainly machine learning methods based on artificial features and deep learning methods for load decomposition. The method based on artificial features has a difficult time obtaining effective load features, leading to low accuracy. The method based on deep learning can automatically extract load characteristics, which improves the accuracy of load decomposition. However, with the deepening of the model structure, the number of parameters becomes too large, the training speed is slow, and the computing cost is high, which leads to the reduction of redundant features and the learning ability in some shallow networks, and the traditional deep learning model has a difficult time obtaining effective features on the time scale. To address these problems, a feature reused long short-term memory multiple output network (M-LSTM) is proposed and used for non-invasive load decomposition tasks. The network proposes an improved multiscale fusion residual module to extract basic load features and proposes the use of LSTM cyclic units to extract time series information. Feature reuse is achieved by combining it with the reorganization of the input data into multiple branches. The proposed structure reduces the difficulty of network optimization, and multi-scale fusion can obtain features on multiple time scales, which improves the ability of model feature extraction. Compared with common network models that tend to train network models for a single target load, the structure can simultaneously decompose the target load power while ensuring the accuracy of load decomposition, thus reducing computational costs, avoiding repetitive model training, and improving training efficiency.