Optimized deep neural network architectures for energy consumption and PV production forecasting

Optimized deep neural network architectures for energy consumption and PV production forecasting

Accurate time-series forecasting of energy consumption and photovoltaic (PV) production is essential for effective energy management and sustainability. Deep Neural Networks (DNNs) are effective tools for learning complex patterns in such data; however, optimizing their architecture remains a signif...

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Bibliographic Details
Main Authors: Eghbal Hosseini, Barzan Saeedpour, Mohsen Banaei, Razgar Ebrahimy
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:Energy Strategy Reviews
Subjects:
Photovoltaic production
Deep neural networks
Meta-heuristic algorithms
Time series forecasting
Online Access:http://www.sciencedirect.com/science/article/pii/S2211467X25000677
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