Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation
The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical...
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MDPI AG
2022-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/8/2855 |
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| author | Taiki Kure Haruka Danil Tsuchiya Yusuke Kameda Hiroki Yamamoto Daisuke Kodaira Junji Kondoh |
| author_facet | Taiki Kure Haruka Danil Tsuchiya Yusuke Kameda Hiroki Yamamoto Daisuke Kodaira Junji Kondoh |
| author_sort | Taiki Kure |
| collection | DOAJ |
| description | The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method had been proposed previously for multiple PV systems using motion estimation. This method forecasts the short time ahead PV power generation by estimating the motion between two geographical images of the distributed PV power systems. In this method, the parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula>, which relates the smoothness of the resulting motion vector field and affects the accuracy of the forecasting, is important. This study focuses on the parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula> and evaluates the effect of changing this parameter on forecasting accuracy. In the periods with drastic power output changes, the forecasting was conducted on 101 PV systems. The results indicate that the absolute mean error of the proposed method with the best parameter is 10.3%, whereas that of the persistence forecasting method is 23.7%. Therefore, the proposed method is effective in forecasting periods when PV output changes drastically within a short time interval. |
| first_indexed | 2024-03-09T13:43:17Z |
| format | Article |
| id | doaj.art-76679bdf8d384941b2e08e7d7cd2f805 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T13:43:17Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-76679bdf8d384941b2e08e7d7cd2f8052023-11-30T21:04:02ZengMDPI AGEnergies1996-10732022-04-01158285510.3390/en15082855Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power GenerationTaiki Kure0Haruka Danil Tsuchiya1Yusuke Kameda2Hiroki Yamamoto3Daisuke Kodaira4Junji Kondoh5Graduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, JapanGraduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, JapanFaculty of Science and Technology, Sophia University, Tokyo 102-8554, JapanGraduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, JapanFaculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba 305-8573, JapanGraduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, JapanThe power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method had been proposed previously for multiple PV systems using motion estimation. This method forecasts the short time ahead PV power generation by estimating the motion between two geographical images of the distributed PV power systems. In this method, the parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula>, which relates the smoothness of the resulting motion vector field and affects the accuracy of the forecasting, is important. This study focuses on the parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula> and evaluates the effect of changing this parameter on forecasting accuracy. In the periods with drastic power output changes, the forecasting was conducted on 101 PV systems. The results indicate that the absolute mean error of the proposed method with the best parameter is 10.3%, whereas that of the persistence forecasting method is 23.7%. Therefore, the proposed method is effective in forecasting periods when PV output changes drastically within a short time interval.https://www.mdpi.com/1996-1073/15/8/2855photovoltaic (PV) power forecastmultiple PV forecastingshort-term PV forecastingmotion estimationoptical flowsmart grid |
| spellingShingle | Taiki Kure Haruka Danil Tsuchiya Yusuke Kameda Hiroki Yamamoto Daisuke Kodaira Junji Kondoh Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation Energies photovoltaic (PV) power forecast multiple PV forecasting short-term PV forecasting motion estimation optical flow smart grid |
| title | Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation |
| title_full | Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation |
| title_fullStr | Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation |
| title_full_unstemmed | Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation |
| title_short | Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation |
| title_sort | parameter evaluation in motion estimation for forecasting multiple photovoltaic power generation |
| topic | photovoltaic (PV) power forecast multiple PV forecasting short-term PV forecasting motion estimation optical flow smart grid |
| url | https://www.mdpi.com/1996-1073/15/8/2855 |
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