Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island
Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/14/2271 |
| _version_ | 1797562405413191680 |
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| author | Jinwoong Park Jihoon Moon Seungmin Jung Eenjun Hwang |
| author_facet | Jinwoong Park Jihoon Moon Seungmin Jung Eenjun Hwang |
| author_sort | Jinwoong Park |
| collection | DOAJ |
| description | Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods. |
| first_indexed | 2024-03-10T18:27:44Z |
| format | Article |
| id | doaj.art-854b7b76e1ec40f79cc8d8b80de356d5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:27:44Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-854b7b76e1ec40f79cc8d8b80de356d52023-11-20T06:51:13ZengMDPI AGRemote Sensing2072-42922020-07-011214227110.3390/rs12142271Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju IslandJinwoong Park0Jihoon Moon1Seungmin Jung2Eenjun Hwang3School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaSchool of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaSchool of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaSchool of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaSmart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.https://www.mdpi.com/2072-4292/12/14/2271smart islandsolar energysolar radiation forecastinglight gradient boosting machinemultistep-ahead predictionfeature importance |
| spellingShingle | Jinwoong Park Jihoon Moon Seungmin Jung Eenjun Hwang Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island Remote Sensing smart island solar energy solar radiation forecasting light gradient boosting machine multistep-ahead prediction feature importance |
| title | Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island |
| title_full | Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island |
| title_fullStr | Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island |
| title_full_unstemmed | Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island |
| title_short | Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island |
| title_sort | multistep ahead solar radiation forecasting scheme based on the light gradient boosting machine a case study of jeju island |
| topic | smart island solar energy solar radiation forecasting light gradient boosting machine multistep-ahead prediction feature importance |
| url | https://www.mdpi.com/2072-4292/12/14/2271 |
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