Prediction Interval Construction Method for Electricity Prices

TL;DR

This paper introduces a novel method for constructing prediction intervals for electricity prices using a conditional GAN to generate scenarios and a reinforcement mechanism to handle volatility, improving uncertainty reflection.

Contribution

It presents a new prediction interval construction approach combining GAN-generated scenarios with a volatility-based reinforcement mechanism, enhancing accuracy and robustness.

Findings

Effective in capturing price uncertainty

Addresses volatile prices and spikes

Provides detailed probability densities

Abstract

Accurate prediction of electricity prices plays an essential role in the electricity market. To reflect the uncertainty of electricity prices, price intervals are predicted. This paper proposes a novel prediction interval construction method. A conditional generative adversarial network is first presented to generate electricity price scenarios, with which the prediction intervals can be constructed. Then, different generated scenarios are stacked to obtain the probability densities, which can be applied to accurately reflect the uncertainty of electricity prices. Furthermore, a reinforced prediction mechanism based on the volatility level of weather factors is introduced to address the spikes or volatile prices. A case study is conducted to verify the effectiveness of the proposed novel prediction interval construction method. The method can also provide the probability density of each price scenario within the prediction interval and has the superiority to address the volatile prices and price spikes with a reinforced prediction mechanism.