Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China
Solar energy is a widely embraced renewable resource, characterized by its unpredictable, fluctuating, and stochastic nature. To mitigate risks and optimize asset utilization cost-effectively, precise analysis and forecasting of solar radiation can prove beneficial. This work aims to provide a hybri...
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| Language: | English |
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Bilijipub publisher
2024-09-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_206710_035782e43493a41f0d613f6905096010.pdf |
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| author | Baran Yılmaz Rachel Samra |
| author_facet | Baran Yılmaz Rachel Samra |
| author_sort | Baran Yılmaz |
| collection | DOAJ |
| description | Solar energy is a widely embraced renewable resource, characterized by its unpredictable, fluctuating, and stochastic nature. To mitigate risks and optimize asset utilization cost-effectively, precise analysis and forecasting of solar radiation can prove beneficial. This work aims to provide a hybrid model using machine learning to accurately predict solar Direct normal irradiance with the least amount of error. In this work, long short-term memory has been optimized using Particle swarm optimization, Grasshopper optimization algorithm, and Slime mold algorithm. SMA-LSTM, which has the best performance result compared to other developed models, is presented as the main method for this work. The data used is from June 1, 2022, to July 30, 2023. Many factors, such as the coefficient of determination, root mean square error, mean absolute percentage error, and mean absolute error have been used in presenting this work, and SMA-LSTM results with the lowest amount of R2 has illustrated acceptable performance. |
| format | Article |
| id | doaj-art-e79a9c13d6894db0ac658c5bf6dfce8e |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-e79a9c13d6894db0ac658c5bf6dfce8e2025-02-12T08:48:04ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-09-010030310412210.22034/aeis.2024.473891.1215206710Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of ChinaBaran Yılmaz0Rachel Samra1Department of Electrical and Energy, Vocational School of Technical Sciences, Konya Technical University, Konya, 42250, TurkeyDepartment of Electronics and Instrumentation, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, 570006, IndiaSolar energy is a widely embraced renewable resource, characterized by its unpredictable, fluctuating, and stochastic nature. To mitigate risks and optimize asset utilization cost-effectively, precise analysis and forecasting of solar radiation can prove beneficial. This work aims to provide a hybrid model using machine learning to accurately predict solar Direct normal irradiance with the least amount of error. In this work, long short-term memory has been optimized using Particle swarm optimization, Grasshopper optimization algorithm, and Slime mold algorithm. SMA-LSTM, which has the best performance result compared to other developed models, is presented as the main method for this work. The data used is from June 1, 2022, to July 30, 2023. Many factors, such as the coefficient of determination, root mean square error, mean absolute percentage error, and mean absolute error have been used in presenting this work, and SMA-LSTM results with the lowest amount of R2 has illustrated acceptable performance.https://aeis.bilijipub.com/article_206710_035782e43493a41f0d613f6905096010.pdfdirect normal irradianceqinghai provincelong short-term memoryslime mould algorithmdni forecasting |
| spellingShingle | Baran Yılmaz Rachel Samra Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China Advances in Engineering and Intelligence Systems direct normal irradiance qinghai province long short-term memory slime mould algorithm dni forecasting |
| title | Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China |
| title_full | Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China |
| title_fullStr | Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China |
| title_full_unstemmed | Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China |
| title_short | Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China |
| title_sort | suggesting a novel hybrid approach for predicting solar irradiance in the qinghai province of china |
| topic | direct normal irradiance qinghai province long short-term memory slime mould algorithm dni forecasting |
| url | https://aeis.bilijipub.com/article_206710_035782e43493a41f0d613f6905096010.pdf |
| work_keys_str_mv | AT baranyılmaz suggestinganovelhybridapproachforpredictingsolarirradianceintheqinghaiprovinceofchina AT rachelsamra suggestinganovelhybridapproachforpredictingsolarirradianceintheqinghaiprovinceofchina |