An Automated Few-Shot Learning for Time-Series Forecasting in Smart Grid Under Data Scarcity

Microgrid can improve greenhouse gas emissions and reduce operational costs. To forecast both energy generation and load demand, time-series prediction has been a key tool in real-time control and optimization. Developing an adequate predictive model is difficult when there is a lack of historical data. Moreover, hyperparameters have a tangible impact on the performance of machine learning models. Bearing these considerations in mind, this article develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a few-shot learning pipeline from a bilevel programming perspective. Specifically, a lower level metalearner helps mitigate the small data challenge, whereas an upper level optimization optimizes both hyperparameters for lower and upper level learners. Note that the proposed framework is designed to accommodate a wide range of machine learning methods, allowing for easy integration through a plug-in mechanism. Comprehensive experiments demonstrate the effectiveness of our proposed BiLO-Auto-TSF/ML to search for a high-performance few-shot learning pipeline for various energy sources.