- home
- Advanced Search
- Energy Research
- 2016-2025
- Energy Research
- 2016-2025
description Publicationkeyboard_double_arrow_right Article 2025Publisher:Elsevier BV Authors:Eghbal Hosseini;
Barzan Saeedpour; Mohsen Banaei;Eghbal Hosseini
Eghbal Hosseini in OpenAIRERazgar Ebrahimy;
Razgar Ebrahimy
Razgar Ebrahimy in OpenAIREAccurate 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 significant challenge. This paper introduces a novel hybrid optimization approach that integrates Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to enhance the DNN architecture for more accurate energy forecasting. The performance of GA-PSO is compared with leading hyperparameter optimization techniques, such as Bayesian Optimization and Evolutionary Strategy, across various optimization benchmarks and DNN hyperparameter tuning tasks. The study evaluates the GA-PSO-enhanced Optimized Deep Neural Network (ODNN) against traditional DNNs and state-of-the-art machine learning methods on multiple real-world energy forecasting tasks. The results demonstrate that ODNN outperforms the average performance of other methods, achieving a 27% improvement in forecasting accuracy and a 22% reduction in error across various metrics. These findings demonstrate the significant potential of GA-PSO as an effective tool to optimize DNN models in energy forecasting applications.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.esr.2025.101704&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.esr.2025.101704&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024Publisher:Elsevier BV Authors:Eghbal Hosseini;
Abbas M. Al-Ghaili; Dler Hussein Kadir; Saraswathy Shamini Gunasekaran; +4 AuthorsEghbal Hosseini
Eghbal Hosseini in OpenAIREEghbal Hosseini;
Abbas M. Al-Ghaili; Dler Hussein Kadir; Saraswathy Shamini Gunasekaran; Ali Najah Ahmed;Eghbal Hosseini
Eghbal Hosseini in OpenAIRENorziana Jamil;
Muhammet Deveci; Rina Azlin Razali;Norziana Jamil
Norziana Jamil in OpenAIREThe synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear nature of energy data. Conversely, meta-heuristic algorithms offer optimization capabilities but suffer from computational burdens, especially with high-dimensional data. This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. We analyze state-of-the-art techniques, innovations, and recent advancements, identifying open research challenges. Additionally, we propose a novel framework that seamlessly merges meta-heuristic algorithms into deep learning paradigms, aiming to enhance performance and efficiency in addressing energy-related problems. The contributions of the paper include:1. Overview of recent advancements in MHs, DL, and integration.2. Coverage of trends from 2018 to 2023.3. Introduction of Alpha metric for performance evaluation.4. Innovative framework harmonizing MHs with DL for energy problems.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.esr.2024.101409&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 18 citations 18 popularity Average influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.esr.2024.101409&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu