AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability

TL;DR

AutoPQ is a novel method that automates the generation of probabilistic forecasts from point forecasts in smart grid applications, improving accuracy while reducing computational effort and environmental impact.

Contribution

AutoPQ introduces an automated approach for probabilistic forecasting using cINN, optimizing model selection and hyperparameters for sustainability in smart grid decision-making.

Findings

AutoPQ outperforms existing probabilistic forecasting methods.

AutoPQ reduces computational effort and environmental impact.

AutoPQ provides transparency on electricity consumption for performance gains.

Abstract

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.