  In this paper, we consider the problem of developing predictive models with
limited data for energy assets such as electricity loads, PV power generations,
etc. We specifically investigate the cases where the amount of historical data
is not sufficient to effectively train the prediction model. We first develop
an energy predictive model based on convolutional neural network (CNN) which is
well suited to capture the interaday, daily, and weekly cyclostationary
patterns, trends and seasonalities in energy assets time series. A transfer
learning strategy is then proposed to address the challenge of limited training
data. We demonstrate our approach on a usecase of daily electricity demand
forecasting. we show practicing the transfer learning strategy on the CNN model
results in significant improvement to existing forecasting methods.
