Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling-Reference-Cited by-同舟云学术

Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling

Published:2021-12 Issue: Volume:237 Page:121543
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Li Wei^ORCID,Becker Denis Mike^ORCID

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference86 articles.

1. Modeling and forecasting electricity loads and prices: a statistical approach;Weron,2006

2. Modelling prices in competitive electricity markets;Bunn,2004

3. Forecasting next-day electricity prices by time series models;Nogales;IEEE Trans Power Syst,2002

4. Forecasting loads and prices in competitive power markets;Bunn;Proc IEEE,2000

5. Electricity price forecasting: a review of the state-of-the-art with a look into the future;Weron;Int J Forecast,2014

Cited by 85 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning-based electricity price forecasting: Findings on price predictability and European electricity markets;Energy;2024-11

2. Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach;Applied Energy;2024-11

3. Day-Ahead electricity price forecasting using a CNN-BiLSTM model in conjunction with autoregressive modeling and hyperparameter optimization;International Journal of Electrical Power & Energy Systems;2024-10

4. Reservoir properties inversion using attention-based parallel hybrid network integrating feature selection and transfer learning;Energy;2024-09

5. Day-ahead electricity price prediction in multi-price zones based on multi-view fusion spatio-temporal graph neural network;Applied Energy;2024-09