Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate Climate
Solar energy is a promising and efficient source of electricity in countries with stable and high sunshine duration. However, in less favorable conditions, for example in continental, temperate climates, the process requires optimization to be cost-effective. This cannot be done without the support...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/7/2764 |
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| author | Karol Postawa Michał Czarnecki Edyta Wrzesińska-Jędrusiak Wieslaw Łyskawiński Marek Kułażyński |
| author_facet | Karol Postawa Michał Czarnecki Edyta Wrzesińska-Jędrusiak Wieslaw Łyskawiński Marek Kułażyński |
| author_sort | Karol Postawa |
| collection | DOAJ |
| description | Solar energy is a promising and efficient source of electricity in countries with stable and high sunshine duration. However, in less favorable conditions, for example in continental, temperate climates, the process requires optimization to be cost-effective. This cannot be done without the support of appropriate mathematical and numerical methods. This work presents a procedure for the construction and optimization of an artificial neural network (ANN), along with an example of its practical application under the conditions mentioned above. In the study, data gathered from a photovoltaic system in 457 consecutive days were utilized. The data includes measurements of generated power, as well as meteorological records. The cascade-forward ANN was trained with a resilient backpropagation procedure and sum squared error as a performance function. The final ANN has two hidden layers with nine and six nodes. This resulted in a relative error of 10.78% and R<sup>2</sup> of 0.92–0.97 depending on the data sample. The case study was used to present an example of the potential application of the tool. This approach proved the real benefits of the optimization of energy consumption. |
| first_indexed | 2024-04-24T10:49:52Z |
| format | Article |
| id | doaj.art-59dd3ea7d8d54565a3d4cb4cdc2cdad2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:49:52Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-59dd3ea7d8d54565a3d4cb4cdc2cdad22024-04-12T13:14:45ZengMDPI AGApplied Sciences2076-34172024-03-01147276410.3390/app14072764Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate ClimateKarol Postawa0Michał Czarnecki1Edyta Wrzesińska-Jędrusiak2Wieslaw Łyskawiński3Marek Kułażyński4Faculty of Chemistry, Wrocław University of Science and Technology, Gdańska 7/9, 50-344 Wrocław, PolandDepartment of Technologies, Institute of Technology and Life Sciences—National Research Institute, Falenty, Hrabska Avenue 3, 05-090 Raszyn, PolandDepartment of Technologies, Institute of Technology and Life Sciences—National Research Institute, Falenty, Hrabska Avenue 3, 05-090 Raszyn, PolandInstitute of Electrical Engineering and Electronics, Poznan University of Technology, 60-965 Poznan, PolandInnovation and Implementation Company Ekomotor Ltd., Wyścigowa 1A, 53-011 Wrocław, PolandSolar energy is a promising and efficient source of electricity in countries with stable and high sunshine duration. However, in less favorable conditions, for example in continental, temperate climates, the process requires optimization to be cost-effective. This cannot be done without the support of appropriate mathematical and numerical methods. This work presents a procedure for the construction and optimization of an artificial neural network (ANN), along with an example of its practical application under the conditions mentioned above. In the study, data gathered from a photovoltaic system in 457 consecutive days were utilized. The data includes measurements of generated power, as well as meteorological records. The cascade-forward ANN was trained with a resilient backpropagation procedure and sum squared error as a performance function. The final ANN has two hidden layers with nine and six nodes. This resulted in a relative error of 10.78% and R<sup>2</sup> of 0.92–0.97 depending on the data sample. The case study was used to present an example of the potential application of the tool. This approach proved the real benefits of the optimization of energy consumption.https://www.mdpi.com/2076-3417/14/7/2764ANNPVsolarrenewable energymodelingcase study |
| spellingShingle | Karol Postawa Michał Czarnecki Edyta Wrzesińska-Jędrusiak Wieslaw Łyskawiński Marek Kułażyński Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate Climate Applied Sciences ANN PV solar renewable energy modeling case study |
| title | Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate Climate |
| title_full | Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate Climate |
| title_fullStr | Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate Climate |
| title_full_unstemmed | Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate Climate |
| title_short | Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate Climate |
| title_sort | cascade forward multi parameter artificial neural networks for predicting the energy efficiency of photovoltaic modules in temperate climate |
| topic | ANN PV solar renewable energy modeling case study |
| url | https://www.mdpi.com/2076-3417/14/7/2764 |
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