Article citationsMore>>

Bendaoud, N. M. M., Farah, N., & Ahmed, S. B. (2021). Comparing Generative Adversarial Networks Architectures for Electricity Demand Forecasting. Energy and Buildings, 247, Article 111152.
https://doi.org/10.1016/j.enbuild.2021.111152

has been cited by the following article:

• TITLE: Carbon Price Prediction for the European Carbon Market Using Generative Adversarial Networks

  AUTHORS: Yuzhi Chen

  KEYWORDS: Carbon Price Prediction, Generative Adversarial Network, Long Short-Term Memory Network, Convolutional Neural Network, Wasser-Stein Distance

  JOURNAL NAME: Modern Economy, Vol.15 No.3, March 18, 2024

  ABSTRACT: Carbon price prediction is an important research interest. Deep learning has latterly realized triumph because of its mighty data processing competence. In this paper, a carbon price forecasting model of generative antagonistic network (GAN) with long short-term memory network (LSTM) as the generator and one-dimensional convolutional neural network (Conv1d) as the discriminator is proposed. The generator inputs historical carbon price data and generates future carbon prices, while the discriminator is designed to differentiate between the real carbon price and the generated carbon price. For verifying the validity of the proposed model, the daily trading price of the European carbon market is selected for numerical simulation, and compared with other prediction models, the GAN proposed has good property in carbon price prediction.