Modellierung und Vorhersage von Gemeinsamen Preisvolumenverteilungen Innerhalb eines Tages Kontinuierlichen Strommarktes

Details

Veröffentlichungs-Nr.

EP4826628

Anmeldetag

13. März 2026

Veröffentlichung

21. Oktober 2026

Rechtsraum

EP

Abstract

The disclosure relates generally to methods and systems for modelling and forecasting of joint price-volume distributions of intraday continuous power markets. Conventional techniques in forecasting the joint P-V behavior of intraday, continuous power markets (IDM) is focused solely on price, overlooking trading time. The methods and systems of the present disclosure propose a hierarchical approach for modeling the joint price-volume (P-V) distribution conditioned on the traded time. The first stage involves learning latent representations of the P-V distributions using a CNN autoencoder, while the second stage leverages these learnt latent representations, along with exogenous variables such as weather and calendar data, to train an LSTM model. The resultant LSTM model outputs are then decoded back to the original distributions using the CNN autoencoder. This advancement enables market participants to fine-tune their trading orders for optimized outcomes and more accurate risk management, profitable trading strategies, and better overall market operation.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien