  As Smart Meters are collecting and transmitting household energy consumption
data to Retail Energy Providers (REP), the main challenge is to ensure the
effective use of fine-grained consumer data while ensuring data privacy. In
this manuscript, we tackle this challenge for energy load consumption
forecasting in regards to REPs which is essential to energy demand management,
load switching and infrastructure development. Specifically, we note that
existing energy load forecasting is centralized, which are not scalable and
most importantly, vulnerable to data privacy threats. Besides, REPs are
individual market participants and liable to ensure the privacy of their own
customers. To address this issue, we propose a novel horizontal
privacy-preserving federated learning framework for REPs energy load
forecasting, namely FedREP. We consider a federated learning system consisting
of a control centre and multiple retailers by enabling multiple REPs to build a
common, robust machine learning model without sharing data, thus addressing
critical issues such as data privacy, data security and scalability. For
forecasting, we use a state-of-the-art Long Short-Term Memory (LSTM) neural
network due to its ability to learn long term sequences of observations and
promises of higher accuracy with time-series data while solving the vanishing
gradient problem. Finally, we conduct extensive data-driven experiments using a
real energy consumption dataset. Experimental results demonstrate that our
proposed federated learning framework can achieve sufficient performance in
terms of MSE ranging between 0.3 to 0.4 and is relatively similar to that of a
centralized approach while preserving privacy and improving scalability.
