Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea
To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/22/8755 |
| _version_ | 1797465367456514048 |
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| author | Yeji Lee Doosung Choi Yongho Jung Myeongjin Ko |
| author_facet | Yeji Lee Doosung Choi Yongho Jung Myeongjin Ko |
| author_sort | Yeji Lee |
| collection | DOAJ |
| description | To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS. |
| first_indexed | 2024-03-09T18:20:33Z |
| format | Article |
| id | doaj.art-625fab2cec9243dfa275e0dee33e708d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T18:20:33Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-625fab2cec9243dfa275e0dee33e708d2023-11-24T08:18:02ZengMDPI AGEnergies1996-10732022-11-011522875510.3390/en15228755Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South KoreaYeji Lee0Doosung Choi1Yongho Jung2Myeongjin Ko3Department of Architectural Design and Engineering, Incheon national University, Incheon 22012, Republic of KoreaDepartment of Building Equipment System and Fire Protection Engineering, Chungwoon University, Incheon 22100, Republic of KoreaDepartment of Building Equipment System and Fire Protection Engineering, Chungwoon University, Incheon 22100, Republic of KoreaDepartment of Building System Technology, Daelim University College, Anyang 13916, Republic of KoreaTo increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS.https://www.mdpi.com/1996-1073/15/22/8755solar radiationspatial interpolationIDWphotovoltaic system |
| spellingShingle | Yeji Lee Doosung Choi Yongho Jung Myeongjin Ko Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea Energies solar radiation spatial interpolation IDW photovoltaic system |
| title | Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea |
| title_full | Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea |
| title_fullStr | Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea |
| title_full_unstemmed | Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea |
| title_short | Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea |
| title_sort | application of technology to develop a framework for predicting power output of a pv system based on a spatial interpolation technique a case study in south korea |
| topic | solar radiation spatial interpolation IDW photovoltaic system |
| url | https://www.mdpi.com/1996-1073/15/22/8755 |
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