  With the employment of smart meters, massive data on consumer behaviour can
be collected by retailers. From the collected data, the retailers may obtain
the household profile information and implement demand response. While
retailers prefer to acquire a model as accurate as possible among different
customers, there are two major challenges. First, different retailers in the
retail market do not share their consumer's electricity consumption data as
these data are regarded as their assets, which has led to the problem of data
island. Second, the electricity load data are highly heterogeneous since
different retailers may serve various consumers. To this end, a fully
distributed short-term load forecasting framework based on a consensus
algorithm and Long Short-Term Memory (LSTM) is proposed, which may protect the
customer's privacy and satisfy the accurate load forecasting requirement.
Specifically, a fully distributed learning framework is exploited for
distributed training, and a consensus technique is applied to meet confidential
privacy. Case studies show that the proposed method has comparable performance
with centralised methods regarding the accuracy, but the proposed method shows
advantages in training speed and data privacy.
