Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data
Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/11/3129 |
| _version_ | 1797566405907841024 |
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| author | Berny Carrera Kwanho Kim |
| author_facet | Berny Carrera Kwanho Kim |
| author_sort | Berny Carrera |
| collection | DOAJ |
| description | Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation. |
| first_indexed | 2024-03-10T19:27:19Z |
| format | Article |
| id | doaj.art-fc28ac31e6194bd8946d83d507906124 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:27:19Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fc28ac31e6194bd8946d83d5079061242023-11-20T02:30:45ZengMDPI AGSensors1424-82202020-06-012011312910.3390/s20113129Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor DataBerny Carrera0Kwanho Kim1Department of Industrial and Management Engineering, Incheon National University, Songdo 22012, KoreaDepartment of Industrial and Management Engineering, Incheon National University, Songdo 22012, KoreaOver the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation.https://www.mdpi.com/1424-8220/20/11/3129data miningmachine learningweather sensorsforecasting solar power generationdeep neural networks |
| spellingShingle | Berny Carrera Kwanho Kim Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data Sensors data mining machine learning weather sensors forecasting solar power generation deep neural networks |
| title | Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data |
| title_full | Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data |
| title_fullStr | Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data |
| title_full_unstemmed | Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data |
| title_short | Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data |
| title_sort | comparison analysis of machine learning techniques for photovoltaic prediction using weather sensor data |
| topic | data mining machine learning weather sensors forecasting solar power generation deep neural networks |
| url | https://www.mdpi.com/1424-8220/20/11/3129 |
| work_keys_str_mv | AT bernycarrera comparisonanalysisofmachinelearningtechniquesforphotovoltaicpredictionusingweathersensordata AT kwanhokim comparisonanalysisofmachinelearningtechniquesforphotovoltaicpredictionusingweathersensordata |