AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability
This addresses the need for efficient and automated uncertainty quantification in smart grid decision-making, though it appears incremental as it builds on existing point forecasting methods with automation enhancements.
The paper tackles the challenge of automating probabilistic forecasting for smart grid operations by introducing AutoPQ, which generates quantile forecasts from point forecasts using a conditional Invertible Neural Network and automates model selection and hyperparameter optimization. The result shows that AutoPQ outperforms state-of-the-art methods while limiting computational effort and environmental impact.
Optimizing smart grid operations relies on critical decision-making informed by uncertainty quantification, making probabilistic forecasting a vital tool. Designing such forecasting models involves three key challenges: accurate and unbiased uncertainty quantification, workload reduction for data scientists during the design process, and limitation of the environmental impact of model training. In order to address these challenges, we introduce AutoPQ, a novel method designed to automate and optimize probabilistic forecasting for smart grid applications. AutoPQ enhances forecast uncertainty quantification by generating quantile forecasts from an existing point forecast by using a conditional Invertible Neural Network (cINN). AutoPQ also automates the selection of the underlying point forecasting method and the optimization of hyperparameters, ensuring that the best model and configuration is chosen for each application. For flexible adaptation to various performance needs and available computing power, AutoPQ comes with a default and an advanced configuration, making it suitable for a wide range of smart grid applications. Additionally, AutoPQ provides transparency regarding the electricity consumption required for performance improvements. We show that AutoPQ outperforms state-of-the-art probabilistic forecasting methods while effectively limiting computational effort and hence environmental impact. Additionally and in the context of sustainability, we quantify the electricity consumption required for performance improvements.