Short-Term Photovoltaic Power Forecasting Based on an Improved Zebra Optimization Algorithm—Stochastic Configuration Network
The output of photovoltaic (PV) power generation systems remains uncertain primarily due to the uncontrollable nature of weather conditions, which may introduce disturbances to the power grid upon integrating PV systems. Accurate short-term PV power forecasting is an essential approach for ensuring...
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| Language: | English |
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3378 |
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| author | Yonggang Wang Wenpeng Li Haoran Chen Yuanchu Ma Bingbing Yu Yadong Yu |
| author_facet | Yonggang Wang Wenpeng Li Haoran Chen Yuanchu Ma Bingbing Yu Yadong Yu |
| author_sort | Yonggang Wang |
| collection | DOAJ |
| description | The output of photovoltaic (PV) power generation systems remains uncertain primarily due to the uncontrollable nature of weather conditions, which may introduce disturbances to the power grid upon integrating PV systems. Accurate short-term PV power forecasting is an essential approach for ensuring the stability of the power system. The paper proposes a short-term PV power forecasting model based on improved zebra optimization algorithm (IZOA)-stochastic configuration network (SCN). First, the historical PV data are divided into three weather patterns, effectively reducing the uncertainty of PV power. Second, a prediction model based on SCN is developed. To enhance the forecasting model’s accuracy even further, the IZOA is introduced to optimize the key parameters of the SCN. Finally, IZOA-SCN is employed for short-term PV power through various weather patterns. Experiment results show that the proposed method significantly improves the prediction accuracy in contrast to other comparison models. |
| format | Article |
| id | doaj-art-4d17b701510e4f889aef4b4f2a39a267 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4d17b701510e4f889aef4b4f2a39a2672025-08-20T03:11:20ZengMDPI AGSensors1424-82202025-05-012511337810.3390/s25113378Short-Term Photovoltaic Power Forecasting Based on an Improved Zebra Optimization Algorithm—Stochastic Configuration NetworkYonggang Wang0Wenpeng Li1Haoran Chen2Yuanchu Ma3Bingbing Yu4Yadong Yu5School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaSchool of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaSchool of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaSchool of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaSchool of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaSchool of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaThe output of photovoltaic (PV) power generation systems remains uncertain primarily due to the uncontrollable nature of weather conditions, which may introduce disturbances to the power grid upon integrating PV systems. Accurate short-term PV power forecasting is an essential approach for ensuring the stability of the power system. The paper proposes a short-term PV power forecasting model based on improved zebra optimization algorithm (IZOA)-stochastic configuration network (SCN). First, the historical PV data are divided into three weather patterns, effectively reducing the uncertainty of PV power. Second, a prediction model based on SCN is developed. To enhance the forecasting model’s accuracy even further, the IZOA is introduced to optimize the key parameters of the SCN. Finally, IZOA-SCN is employed for short-term PV power through various weather patterns. Experiment results show that the proposed method significantly improves the prediction accuracy in contrast to other comparison models.https://www.mdpi.com/1424-8220/25/11/3378stochastic configuration networkzebra optimization algorithmphotovoltaic powershort-term photovoltaic power forecasting |
| spellingShingle | Yonggang Wang Wenpeng Li Haoran Chen Yuanchu Ma Bingbing Yu Yadong Yu Short-Term Photovoltaic Power Forecasting Based on an Improved Zebra Optimization Algorithm—Stochastic Configuration Network Sensors stochastic configuration network zebra optimization algorithm photovoltaic power short-term photovoltaic power forecasting |
| title | Short-Term Photovoltaic Power Forecasting Based on an Improved Zebra Optimization Algorithm—Stochastic Configuration Network |
| title_full | Short-Term Photovoltaic Power Forecasting Based on an Improved Zebra Optimization Algorithm—Stochastic Configuration Network |
| title_fullStr | Short-Term Photovoltaic Power Forecasting Based on an Improved Zebra Optimization Algorithm—Stochastic Configuration Network |
| title_full_unstemmed | Short-Term Photovoltaic Power Forecasting Based on an Improved Zebra Optimization Algorithm—Stochastic Configuration Network |
| title_short | Short-Term Photovoltaic Power Forecasting Based on an Improved Zebra Optimization Algorithm—Stochastic Configuration Network |
| title_sort | short term photovoltaic power forecasting based on an improved zebra optimization algorithm stochastic configuration network |
| topic | stochastic configuration network zebra optimization algorithm photovoltaic power short-term photovoltaic power forecasting |
| url | https://www.mdpi.com/1424-8220/25/11/3378 |
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