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Non Intrusive Load Monitoring (NILM) or Energy Disaggregation (ED), seeks tosave energy by decomposing corresponding appliances power reading from anaggregate power reading of the whole house. It is a single channel blind sourceseparation problem (SCBSS) and difficult prediction problem because it isunidentifiable. Recent research shows that deep learning has become a growingpopularity for NILM problem. The ability of neural networks to extract loadfeatures is closely related to its depth. However, deep neural network isdifficult to train because of exploding gradient, vanishing gradient andnetwork degradation. To solve these problems, we propose a sequence to pointlearning framework based on bidirectional (non-casual) dilated convolution forNILM. To be more convincing, we compare our method with the state of artmethod, Seq2point (Zhang) directly and compare with existing algorithmsindirectly via two same datasets and metrics. Experiments based on REDD andUK-DALE data sets show that our proposed approach is far superior to existingapproaches in all appliances.

Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring