Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information
Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energ...
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/4/901 |
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doaj-c741956d5254499e9de5916d91bf6bd92020-11-25T01:45:09ZengMDPI AGEnergies1996-10732020-02-0113490110.3390/en13040901en13040901Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological InformationMedine Colak0Mehmet Yesilbudak1Ramazan Bayindir2Department of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, Ankara 06500, TurkeyDepartment of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nevsehir Haci Bektas Veli University, Nevsehir 50300, TurkeyDepartment of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, Ankara 06500, TurkeySolar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.https://www.mdpi.com/1996-1073/13/4/901photovoltaic powermeteorological inputmetaheuristic optimizationartificial neural networksprediction |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Medine Colak Mehmet Yesilbudak Ramazan Bayindir |
| spellingShingle |
Medine Colak Mehmet Yesilbudak Ramazan Bayindir Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information Energies photovoltaic power meteorological input metaheuristic optimization artificial neural networks prediction |
| author_facet |
Medine Colak Mehmet Yesilbudak Ramazan Bayindir |
| author_sort |
Medine Colak |
| title |
Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information |
| title_short |
Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information |
| title_full |
Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information |
| title_fullStr |
Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information |
| title_full_unstemmed |
Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information |
| title_sort |
daily photovoltaic power prediction enhanced by hybrid gwo-mlp, alo-mlp and woa-mlp models using meteorological information |
| publisher |
MDPI AG |
| series |
Energies |
| issn |
1996-1073 |
| publishDate |
2020-02-01 |
| description |
Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions. |
| topic |
photovoltaic power meteorological input metaheuristic optimization artificial neural networks prediction |
| url |
https://www.mdpi.com/1996-1073/13/4/901 |
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AT medinecolak dailyphotovoltaicpowerpredictionenhancedbyhybridgwomlpalomlpandwoamlpmodelsusingmeteorologicalinformation AT mehmetyesilbudak dailyphotovoltaicpowerpredictionenhancedbyhybridgwomlpalomlpandwoamlpmodelsusingmeteorologicalinformation AT ramazanbayindir dailyphotovoltaicpowerpredictionenhancedbyhybridgwomlpalomlpandwoamlpmodelsusingmeteorologicalinformation |
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