  While the advanced machine learning algorithms are effective in load
forecasting, they often suffer from low data utilization, and hence their
superior performance relies on massive datasets. This motivates us to design
machine learning algorithms with improved data utilization. Specifically, we
consider the load forecasting for a new user in the system by observing only
few shots (data points) of its energy consumption. This task is challenging
since the limited samples are insufficient to exploit the temporal
characteristics, essential for load forecasting. Nonetheless, we notice that
there are not too many temporal characteristics for residential loads due to
the limited kinds of human lifestyle. Hence, we propose to utilize the
historical load profile data from existing users to conduct effective
clustering, which mitigates the challenges brought by the limited samples.
Specifically, we first design a feature extraction clustering method for
categorizing historical data. Then, inheriting the prior knowledge from the
clustering results, we propose a two-phase Long Short Term Memory (LSTM) model
to conduct load forecasting for new users. The proposed method outperforms the
traditional LSTM model, especially when the training sample size fails to cover
a whole period (i.e., 24 hours in our task). Extensive case studies on two
real-world datasets and one synthetic dataset verify the effectiveness and
efficiency of our method.
