Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration

Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration

Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to...

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Bibliographic Details
Main Authors: Ziyang Wang, Masahiro Mae, Takeshi Yamane, Masato Ajisaka, Tatsuya Nakata, Ryuji Matsuhashi
Format: Article
Language:English
Published: MDPI AG 2024-06-01
Series:Energies
Subjects:
day-ahead electricity price forecasting (DAEPF)
renewable energy sources (RES)
CNN–LSTM
deep learning
ensemble learning
zero day-ahead electricity price
Online Access:https://www.mdpi.com/1996-1073/17/11/2687

Internet

https://www.mdpi.com/1996-1073/17/11/2687

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