Automated Few-Shot Time Series Forecasting based on Bi-level Programming
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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.
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