A deep generative model for probabilistic energy forecasting in power systems: normalizing flows

TL;DR

This paper introduces the use of normalizing flows, a deep generative model, for probabilistic energy forecasting in power systems, demonstrating its effectiveness in modeling complex renewable energy data.

Contribution

It applies normalizing flows to power system forecasting, showing they outperform or match existing deep generative models like GANs and VAEs in accuracy and reliability.

Findings

Normalizing flows effectively model multivariate stochastic distributions.

The approach is competitive with GANs and VAEs in forecasting accuracy.

Numerical experiments are simple, reproducible, and demonstrate practical utility.

Abstract

Greater direct electrification of end-use sectors with a higher share of renewables is one of the pillars to power a carbon-neutral society by 2050. However, in contrast to conventional power plants, renewable energy is subject to uncertainty raising challenges for their interaction with power systems. Scenario-based probabilistic forecasting models have become a vital tool to equip decision-makers. This paper presents to the power systems forecasting practitioners a recent deep learning technique, the normalizing flows, to produce accurate scenario-based probabilistic forecasts that are crucial to face the new challenges in power systems applications. The strength of this technique is to directly learn the stochastic multivariate distribution of the underlying process by maximizing the likelihood. Through comprehensive empirical evaluations using the open data of the Global Energy Forecasting Competition 2014, we demonstrate that this methodology is competitive with other state-of-the-art deep learning generative models: generative adversarial networks and variational autoencoders. The models producing weather-based wind, solar power, and load scenarios are properly compared in terms of forecast value by considering the case study of an energy retailer and quality using several complementary metrics. The numerical experiments are simple and easily reproducible. Thus, we hope it will encourage other forecasting practitioners to test and use normalizing flows in power system applications such as bidding on electricity markets, scheduling power systems with high renewable energy sources penetration, energy management of virtual power plan or microgrids, and unit commitment.