Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach
The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and...
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Elsevier
2024-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671124004327 |
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| author | Muhammad Saud Ul Hassan Kashif Liaqat Laura Schaefer Alexander J. Zolan |
| author_facet | Muhammad Saud Ul Hassan Kashif Liaqat Laura Schaefer Alexander J. Zolan |
| author_sort | Muhammad Saud Ul Hassan |
| collection | DOAJ |
| description | The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters. |
| format | Article |
| id | doaj-art-eb865a2aae584ee08314ea4e374dd244 |
| institution | OA Journals |
| issn | 2772-6711 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-eb865a2aae584ee08314ea4e374dd2442025-08-20T01:56:41ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112024-12-011010085310.1016/j.prime.2024.100853Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approachMuhammad Saud Ul Hassan0Kashif Liaqat1Laura Schaefer2Alexander J. Zolan3Department of Mechanical Engineering, Rice University, Houston TX, 77005, United StatesDepartment of Mechanical Engineering, Rice University, Houston TX, 77005, United States; Corresponding author.Department of Mechanical Engineering, Rice University, Houston TX, 77005, United StatesThermal Energy Systems, National Renewable Energy Laboratory, Golden CO, 80401, United StatesThe escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.http://www.sciencedirect.com/science/article/pii/S2772671124004327Direct Normal IrradianceConcentrated solar powerDeep neural networksRecurrent neural networksTransformers |
| spellingShingle | Muhammad Saud Ul Hassan Kashif Liaqat Laura Schaefer Alexander J. Zolan Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach e-Prime: Advances in Electrical Engineering, Electronics and Energy Direct Normal Irradiance Concentrated solar power Deep neural networks Recurrent neural networks Transformers |
| title | Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach |
| title_full | Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach |
| title_fullStr | Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach |
| title_full_unstemmed | Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach |
| title_short | Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach |
| title_sort | modern deep neural networks for direct normal irradiance forecasting a classification approach |
| topic | Direct Normal Irradiance Concentrated solar power Deep neural networks Recurrent neural networks Transformers |
| url | http://www.sciencedirect.com/science/article/pii/S2772671124004327 |
| work_keys_str_mv | AT muhammadsaudulhassan moderndeepneuralnetworksfordirectnormalirradianceforecastingaclassificationapproach AT kashifliaqat moderndeepneuralnetworksfordirectnormalirradianceforecastingaclassificationapproach AT lauraschaefer moderndeepneuralnetworksfordirectnormalirradianceforecastingaclassificationapproach AT alexanderjzolan moderndeepneuralnetworksfordirectnormalirradianceforecastingaclassificationapproach |