  New micro-grid design with renewable energy sources and battery storage
systems can help improve greenhouse gas emissions and reduce the operational
cost. To provide an effective short-/long-term forecasting of both energy
generation and load demand, time series predictive modeling has been one of the
key tools to guide the optimal decision-making for planning and operation. One
of the critical challenges of time series renewable energy forecasting is the
lack of historical data to train an adequate predictive model. Moreover, the
performance of a machine learning model is sensitive to the choice of its
corresponding hyperparameters. Bearing these considerations in mind, this paper
develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a
few-shot learning pipeline from a bi-level programming perspective.
Specifically, the lower-level meta-learning helps boost the base-learner to
mitigate the small data challenge while the hyperparameter optimization at the
upper level proactively searches for the optimal hyperparameter configurations
for both base- and meta-learners. Note that the proposed framework is so
general that any off-the-shelf machine learning method can be used in a plug-in
manner. Comprehensive experiments fully demonstrate the effectiveness of our
proposed BiLO-Auto-TSF/ML framework to search for a high-performance few-shot
learning pipeline for various energy sources.
