UC Riverside

Nonintrusive load monitoring (NILM) is one of the key applications of big data analytics in smart power distribution systems for end-use customers. A successful implementation of nonintrusive load monitoring can improve the knowledge of load, and has great potential in increasing demand side response. Traditional nonintrusive load monitoring algorithms have long suffered from the problems of high misjudgment rate and low accuracy of disaggregated power value. To address these problems, the deep learning framework was adopted. Specifically, a nonintrusive load monitoring model based on sequence-to-sequence (seq2seq) model with attention mechanism was proposed. The model first embeds the input active power time sequence into a high dimensional vector, extracts information with a long short term memory (LSTM)-based encoder, and then selects the most relevant information to decode and reaches the final disaggregation results with a decoder wrapped by attention mechanism. Compared with existing models, the proposed deep learning network structure increases model's ability to extract and utilize information dramatically. The proposed model was tested on the REFITPowerData dataset, and compared with the state-of-the-art model.