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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | Energy Strategy Reviews |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X25000677 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850226339472211968 |
|---|---|
| author | Eghbal Hosseini Barzan Saeedpour Mohsen Banaei Razgar Ebrahimy |
| author_facet | Eghbal Hosseini Barzan Saeedpour Mohsen Banaei Razgar Ebrahimy |
| author_sort | Eghbal Hosseini |
| collection | DOAJ |
| description | 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 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. |
| format | Article |
| id | doaj-art-019e191f4b5547eea63e7fa29a8d3d9d |
| institution | OA Journals |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj-art-019e191f4b5547eea63e7fa29a8d3d9d2025-08-20T02:05:07ZengElsevierEnergy Strategy Reviews2211-467X2025-05-015910170410.1016/j.esr.2025.101704Optimized deep neural network architectures for energy consumption and PV production forecastingEghbal Hosseini0Barzan Saeedpour1Mohsen Banaei2Razgar Ebrahimy3Technical University of Denmark, Department of Applied Mathematics and Computer Science, Copenhagen, Denmark; Corresponding author.Department of Computer Engineering, University of Kurdistan, IranTechnical University of Denmark, Department of Applied Mathematics and Computer Science, Copenhagen, DenmarkTechnical University of Denmark, Department of Applied Mathematics and Computer Science, Copenhagen, DenmarkAccurate 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.http://www.sciencedirect.com/science/article/pii/S2211467X25000677Photovoltaic productionDeep neural networksMeta-heuristic algorithmsTime series forecasting |
| spellingShingle | Eghbal Hosseini Barzan Saeedpour Mohsen Banaei Razgar Ebrahimy Optimized deep neural network architectures for energy consumption and PV production forecasting Energy Strategy Reviews Photovoltaic production Deep neural networks Meta-heuristic algorithms Time series forecasting |
| title | Optimized deep neural network architectures for energy consumption and PV production forecasting |
| title_full | Optimized deep neural network architectures for energy consumption and PV production forecasting |
| title_fullStr | Optimized deep neural network architectures for energy consumption and PV production forecasting |
| title_full_unstemmed | Optimized deep neural network architectures for energy consumption and PV production forecasting |
| title_short | Optimized deep neural network architectures for energy consumption and PV production forecasting |
| title_sort | optimized deep neural network architectures for energy consumption and pv production forecasting |
| topic | Photovoltaic production Deep neural networks Meta-heuristic algorithms Time series forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X25000677 |
| work_keys_str_mv | AT eghbalhosseini optimizeddeepneuralnetworkarchitecturesforenergyconsumptionandpvproductionforecasting AT barzansaeedpour optimizeddeepneuralnetworkarchitecturesforenergyconsumptionandpvproductionforecasting AT mohsenbanaei optimizeddeepneuralnetworkarchitecturesforenergyconsumptionandpvproductionforecasting AT razgarebrahimy optimizeddeepneuralnetworkarchitecturesforenergyconsumptionandpvproductionforecasting |