Devising single in-out long short-term memory univariate models for predicting the electricity price on the day-ahead markets

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-09-04 DOI:10.1080/09540091.2024.2397351

Adela Bâra, Simona Vasilica Oprea

引用次数: 0

Abstract

We investigate the performance of intelligent systems such as various Long Short-Term Memory (LSTM) and hybrid models to forecast the electricity spot prices considering univariate and multivariate...

查看原文本刊更多论文

设计用于预测日前市场电价的单进单出长短期记忆单变量模型

我们研究了智能系统的性能，如各种长短期记忆（LSTM）和混合模型，以预测考虑到单变量和多变量的电力现货价格。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.