AI-driven demand forecasting for enhanced energy management in renewable microgrids: A hybrid LSTM-CNN approach

AI-driven demand forecasting for enhanced energy management in renewable microgrids: A hybrid LSTM-CNN approach

Aim/Background: The increasing integration of renewable energy sources into microgrids underscores the need for accurate demand forecasting to ensure stable and efficient microgrid operation. However, the inherent unpredictability of renewable energy sources presents significant challenges in energy...

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Bibliographic Details
Main Authors: Bashiru olalekan ariyo, mutalub adesina lambe, olalekan ogunbiyi, musa abdulwaheed, bilkisu jimada ojuolape, monsurat omolara balogun
Format: Article
Language:English
Published: Eldaghayes Publisher 2025-01-01
Series:Journal of Engineering Research and Reviews
Subjects:
ai-driven demand forecasting
long short-term memory (lstm) networks; renewable microgrids; energy management; convolutional neural networks (cnns); forecasting accuracy
Online Access:http://www.ejmanager.com/fulltextpdf.php?mno=223619
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