  A smart home energy dataset that records miscellaneous energy consumption
data is publicly offered. The proposed energy activity dataset (EAD) has a high
data type diversity in contrast to existing load monitoring datasets. In EAD, a
simple data point is labeled with the appliance, brand, and event information,
whereas a complex data point has an extra application label. Several
discoveries have been made on the energy consumption patterns of many
appliances. Load curves of the appliances are measured when different events
and applications are triggered and utilized. A revised
longest-common-subsequence (LCS) similarity measurement algorithm is proposed
to calculate energy dataset similarities. Thus, the data quality prior
information becomes available before training machine learning models. In
addition, a subsample convolutional neural network (SCNN) is put forward. It
serves as a non-intrusive optical character recognition (OCR) approach to
obtain energy data directly from monitors of power meters. The link for the EAD
dataset is:
https://drive.google.com/drive/folders/1zn0V6Q8eXXSKxKgcs8ZRValL5VEn3anD
