Integrating Machine Learning Algorithms for Predicting Solar Power Generation
In recent years, there has been a growing interest in using artificial intelligence (AI) techniques to predict solar power generation. One such technique is the use of an artificial neural network (ANN) with a genetic algorithm (GA) to optimize its parameters. This approach involves training an ANN...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/24/e3sconf_icseret2023_01004.pdf |
| _version_ | 1797808096685326336 |
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| author | Sangeetha K. Liz Anitha Sofia P. Suganthi Femilinjana D. Little |
| author_facet | Sangeetha K. Liz Anitha Sofia P. Suganthi Femilinjana D. Little |
| author_sort | Sangeetha K. |
| collection | DOAJ |
| description | In recent years, there has been a growing interest in using artificial intelligence (AI) techniques to predict solar power generation. One such technique is the use of an artificial neural network (ANN) with a genetic algorithm (GA) to optimize its parameters. This approach involves training an ANN to predict solar power generation based on historical data and using a GA to optimize the ANN’s architecture and activation function. The GA searches for the best combination of hidden layers and activation functions to minimize the error between the predicted and actual solar power generation. This paper presents an algorithm for implementing an ANN-GA for predicting solar power generation. The algorithm involves preprocessing the data, defining the ANN architecture, defining the fitness function, and implementing the GA to optimize the ANN’s parameters. The results of this approach can be useful for predicting future solar power generation and optimizing the performance of solar power systems. |
| first_indexed | 2024-03-13T06:32:26Z |
| format | Article |
| id | doaj.art-e553595dff1645a881a7bb172904e4d3 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:32:26Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-e553595dff1645a881a7bb172904e4d32023-06-09T09:06:52ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013870100410.1051/e3sconf/202338701004e3sconf_icseret2023_01004Integrating Machine Learning Algorithms for Predicting Solar Power GenerationSangeetha K.0Liz Anitha Sofia1P. Suganthi2Femilinjana D. Little3Bannari Amman Institute of TechnologyBannari Amman Institute of TechnologyAssistant Professor, Prince Dr. K. Vasudevan College of Engineering and TechnologyAssistant Professor, Prince Shri Venkateshwara Padmavathy Engineering CollegeIn recent years, there has been a growing interest in using artificial intelligence (AI) techniques to predict solar power generation. One such technique is the use of an artificial neural network (ANN) with a genetic algorithm (GA) to optimize its parameters. This approach involves training an ANN to predict solar power generation based on historical data and using a GA to optimize the ANN’s architecture and activation function. The GA searches for the best combination of hidden layers and activation functions to minimize the error between the predicted and actual solar power generation. This paper presents an algorithm for implementing an ANN-GA for predicting solar power generation. The algorithm involves preprocessing the data, defining the ANN architecture, defining the fitness function, and implementing the GA to optimize the ANN’s parameters. The results of this approach can be useful for predicting future solar power generation and optimizing the performance of solar power systems.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/24/e3sconf_icseret2023_01004.pdfmachine learning algorithmssolar power generationrenewable energy sourcespredictive modelingartificial neural networkssupport vector machines |
| spellingShingle | Sangeetha K. Liz Anitha Sofia P. Suganthi Femilinjana D. Little Integrating Machine Learning Algorithms for Predicting Solar Power Generation E3S Web of Conferences machine learning algorithms solar power generation renewable energy sources predictive modeling artificial neural networks support vector machines |
| title | Integrating Machine Learning Algorithms for Predicting Solar Power Generation |
| title_full | Integrating Machine Learning Algorithms for Predicting Solar Power Generation |
| title_fullStr | Integrating Machine Learning Algorithms for Predicting Solar Power Generation |
| title_full_unstemmed | Integrating Machine Learning Algorithms for Predicting Solar Power Generation |
| title_short | Integrating Machine Learning Algorithms for Predicting Solar Power Generation |
| title_sort | integrating machine learning algorithms for predicting solar power generation |
| topic | machine learning algorithms solar power generation renewable energy sources predictive modeling artificial neural networks support vector machines |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/24/e3sconf_icseret2023_01004.pdf |
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