A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation
Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model fo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/13/2605 |
| _version_ | 1797527870979964928 |
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| author | Bowoo Kim Dongjun Suh Marc-Oliver Otto Jeung-Soo Huh |
| author_facet | Bowoo Kim Dongjun Suh Marc-Oliver Otto Jeung-Soo Huh |
| author_sort | Bowoo Kim |
| collection | DOAJ |
| description | Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model. |
| first_indexed | 2024-03-10T09:49:58Z |
| format | Article |
| id | doaj.art-8c9d173e73474a19a14fe1f16396022a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:49:58Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8c9d173e73474a19a14fe1f16396022a2023-11-22T02:49:44ZengMDPI AGRemote Sensing2072-42922021-07-011313260510.3390/rs13132605A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic GenerationBowoo Kim0Dongjun Suh1Marc-Oliver Otto2Jeung-Soo Huh3Department of Convergence & Fusion System Engineering, Kyungpook Nation University, Sangju 37224, KoreaDepartment of Convergence & Fusion System Engineering, Kyungpook Nation University, Sangju 37224, KoreaDepartment of Mathematics, Natural and Economic Science, Ulm University of Applied Science, Prittwitzstr, 10, 89075 Ulm, GermanyDepartment of Convergence & Fusion System Engineering, Kyungpook Nation University, Sangju 37224, KoreaCurrently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model.https://www.mdpi.com/2072-4292/13/13/2605multisitesolar PV generationspatio-temporalpredictionmachine learningsatellite image |
| spellingShingle | Bowoo Kim Dongjun Suh Marc-Oliver Otto Jeung-Soo Huh A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation Remote Sensing multisite solar PV generation spatio-temporal prediction machine learning satellite image |
| title | A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation |
| title_full | A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation |
| title_fullStr | A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation |
| title_full_unstemmed | A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation |
| title_short | A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation |
| title_sort | novel hybrid spatio temporal forecasting of multisite solar photovoltaic generation |
| topic | multisite solar PV generation spatio-temporal prediction machine learning satellite image |
| url | https://www.mdpi.com/2072-4292/13/13/2605 |
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