Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks
Solar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods—gen...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/22/8669 |
| _version_ | 1797465343760793600 |
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| author | Ali Kamil Gumar Funda Demir |
| author_facet | Ali Kamil Gumar Funda Demir |
| author_sort | Ali Kamil Gumar |
| collection | DOAJ |
| description | Solar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods—genetic algorithm, particle swarm optimization, and artificial bee colony—to optimize artificial neural network weights for predicting solar power. The built artificial neural network was used to predict photovoltaic power depending on the measured features. The data were collected and stored as structured data (Excel file). The results from using the three methods have shown that the optimization is very effective. The results showed that particle swarm optimization outperformed the genetic algorithm and artificial bee colony. |
| first_indexed | 2024-03-09T18:21:11Z |
| format | Article |
| id | doaj.art-0bee4e40c0cd453384a923b98f62e90b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T18:21:11Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0bee4e40c0cd453384a923b98f62e90b2023-11-24T08:16:43ZengMDPI AGEnergies1996-10732022-11-011522866910.3390/en15228669Solar Photovoltaic Power Estimation Using Meta-Optimized Neural NetworksAli Kamil Gumar0Funda Demir1Department of Mechatronics Engineering, Faculty of Engineering, Karabuk University, 78050 Karabuk, TurkeyDepartment of Mechatronics Engineering, Faculty of Engineering, Karabuk University, 78050 Karabuk, TurkeySolar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods—genetic algorithm, particle swarm optimization, and artificial bee colony—to optimize artificial neural network weights for predicting solar power. The built artificial neural network was used to predict photovoltaic power depending on the measured features. The data were collected and stored as structured data (Excel file). The results from using the three methods have shown that the optimization is very effective. The results showed that particle swarm optimization outperformed the genetic algorithm and artificial bee colony.https://www.mdpi.com/1996-1073/15/22/8669artificial neural network (ANN)artificial bee colony (ABC)genetic algorithm (GA)particle swarm optimization (PSO)solar photovoltaic (PV) |
| spellingShingle | Ali Kamil Gumar Funda Demir Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks Energies artificial neural network (ANN) artificial bee colony (ABC) genetic algorithm (GA) particle swarm optimization (PSO) solar photovoltaic (PV) |
| title | Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks |
| title_full | Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks |
| title_fullStr | Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks |
| title_full_unstemmed | Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks |
| title_short | Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks |
| title_sort | solar photovoltaic power estimation using meta optimized neural networks |
| topic | artificial neural network (ANN) artificial bee colony (ABC) genetic algorithm (GA) particle swarm optimization (PSO) solar photovoltaic (PV) |
| url | https://www.mdpi.com/1996-1073/15/22/8669 |
| work_keys_str_mv | AT alikamilgumar solarphotovoltaicpowerestimationusingmetaoptimizedneuralnetworks AT fundademir solarphotovoltaicpowerestimationusingmetaoptimizedneuralnetworks |