Improved Surface Solar Irradiation Estimation Using Satellite Data and Feature Engineering
Planning an optimal installation site to maximize power-generation efficiency is crucial for the effective operation of photovoltaic power plants. Achieving this requires accurate, reliable information on solar irradiation across different regions. However, ground-based measurements using pyranomete...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/65 |
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| author | Jinyong Kim Eunkyeong Kim Seunghwan Jung Minseok Kim Baekcheon Kim Sungshin Kim |
| author_facet | Jinyong Kim Eunkyeong Kim Seunghwan Jung Minseok Kim Baekcheon Kim Sungshin Kim |
| author_sort | Jinyong Kim |
| collection | DOAJ |
| description | Planning an optimal installation site to maximize power-generation efficiency is crucial for the effective operation of photovoltaic power plants. Achieving this requires accurate, reliable information on solar irradiation across different regions. However, ground-based measurements using pyranometers are resource-intensive, requiring substantial time and human effort, and their measurement range is limited, complicating data collection. To address this, we propose a method to accurately estimate surface solar irradiation (SSI) using satellite data and feature engineering. By leveraging satellite data as the primary input, we overcome the spatial and temporal limitations of ground-based measurements. Additionally, we improve SSI-estimation performance through designed features based on the geometric information of the Sun and satellite. A hybrid deep neural network model is used for SSI estimation, effectively handling data of varying dimensions. Hourly SSI data from 12 synoptic observation stations collected over one year, excluded from the model’s training and validation sets, are utilized to evaluate the proposed method. Experimental results demonstrate strong SSI-estimation performance, with an average root mean square error (RMSE) of 0.1813 MJ/m<sup>2</sup>, a relative RMSE of 0.1601, mean absolute error of 0.1159 MJ/m<sup>2</sup>, and coefficient of determination of 0.9680. |
| format | Article |
| id | doaj-art-97402ecd37664d58866125f8c168685a |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-97402ecd37664d58866125f8c168685a2025-01-10T13:20:06ZengMDPI AGRemote Sensing2072-42922024-12-011716510.3390/rs17010065Improved Surface Solar Irradiation Estimation Using Satellite Data and Feature EngineeringJinyong Kim0Eunkyeong Kim1Seunghwan Jung2Minseok Kim3Baekcheon Kim4Sungshin Kim5Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaPlanning an optimal installation site to maximize power-generation efficiency is crucial for the effective operation of photovoltaic power plants. Achieving this requires accurate, reliable information on solar irradiation across different regions. However, ground-based measurements using pyranometers are resource-intensive, requiring substantial time and human effort, and their measurement range is limited, complicating data collection. To address this, we propose a method to accurately estimate surface solar irradiation (SSI) using satellite data and feature engineering. By leveraging satellite data as the primary input, we overcome the spatial and temporal limitations of ground-based measurements. Additionally, we improve SSI-estimation performance through designed features based on the geometric information of the Sun and satellite. A hybrid deep neural network model is used for SSI estimation, effectively handling data of varying dimensions. Hourly SSI data from 12 synoptic observation stations collected over one year, excluded from the model’s training and validation sets, are utilized to evaluate the proposed method. Experimental results demonstrate strong SSI-estimation performance, with an average root mean square error (RMSE) of 0.1813 MJ/m<sup>2</sup>, a relative RMSE of 0.1601, mean absolute error of 0.1159 MJ/m<sup>2</sup>, and coefficient of determination of 0.9680.https://www.mdpi.com/2072-4292/17/1/65surface solar irradiation estimationsatellite datafeature engineeringhybrid deep neural networksolar geometry |
| spellingShingle | Jinyong Kim Eunkyeong Kim Seunghwan Jung Minseok Kim Baekcheon Kim Sungshin Kim Improved Surface Solar Irradiation Estimation Using Satellite Data and Feature Engineering Remote Sensing surface solar irradiation estimation satellite data feature engineering hybrid deep neural network solar geometry |
| title | Improved Surface Solar Irradiation Estimation Using Satellite Data and Feature Engineering |
| title_full | Improved Surface Solar Irradiation Estimation Using Satellite Data and Feature Engineering |
| title_fullStr | Improved Surface Solar Irradiation Estimation Using Satellite Data and Feature Engineering |
| title_full_unstemmed | Improved Surface Solar Irradiation Estimation Using Satellite Data and Feature Engineering |
| title_short | Improved Surface Solar Irradiation Estimation Using Satellite Data and Feature Engineering |
| title_sort | improved surface solar irradiation estimation using satellite data and feature engineering |
| topic | surface solar irradiation estimation satellite data feature engineering hybrid deep neural network solar geometry |
| url | https://www.mdpi.com/2072-4292/17/1/65 |
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