  Non Intrusive Load Monitoring (NILM) or Energy Disaggregation (ED), seeks to
save energy by decomposing corresponding appliances power reading from an
aggregate power reading of the whole house. It is a single channel blind source
separation problem (SCBSS) and difficult prediction problem because it is
unidentifiable. Recent research shows that deep learning has become a growing
popularity for NILM problem. The ability of neural networks to extract load
features is closely related to its depth. However, deep neural network is
difficult to train because of exploding gradient, vanishing gradient and
network degradation. To solve these problems, we propose a sequence to point
learning framework based on bidirectional (non-casual) dilated convolution for
NILM. To be more convincing, we compare our method with the state of art
method, Seq2point (Zhang) directly and compare with existing algorithms
indirectly via two same datasets and metrics. Experiments based on REDD and
UK-DALE data sets show that our proposed approach is far superior to existing
approaches in all appliances.
