Adaptive Fusion Feature Transfer Learning Method For NILM
Keqin Li, Jian Feng, Juan Zhang, Qi Xiao
Abstract
Non-invasive load monitoring (NILM) aims to extract the power consumption of individual appliances from a smart meter that measures the total power consumption of all appliances. At present, deep learning methods have achieved leading results. However, the need for a large number of training data and the poor generalization ability of models limit the further development of NILM. To break through these limitations, this paper proposes an adaptive fusion feature transfer learning method. Firstly, to provide rich features for feature transfer, multiple feature extraction branches are used to extract temporal and spatial features from different perspectives. These features are fused for adaptive adjustment. Secondly, the attention mechanism is used to adapt the features extracted from the model of the source task so that they are more conducive to the model of the target task. Finally, abundant simulation experiments are performed for appliance transfer and house transfer, respectively. It is verified that the proposed method can achieve better results than the existing method by using only a small amount of training data and retraining a part of the new model.