A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy
The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/15/4224 |
| _version_ | 1797560931876601856 |
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| author | Guillermo Almonacid-Olleros Gabino Almonacid Juan Ignacio Fernandez-Carrasco Macarena Espinilla-Estevez Javier Medina-Quero |
| author_facet | Guillermo Almonacid-Olleros Gabino Almonacid Juan Ignacio Fernandez-Carrasco Macarena Espinilla-Estevez Javier Medina-Quero |
| author_sort | Guillermo Almonacid-Olleros |
| collection | DOAJ |
| description | The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance. |
| first_indexed | 2024-03-10T18:07:31Z |
| format | Article |
| id | doaj.art-97fb94906dfd45fbaf5e524e3ad428eb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:07:31Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-97fb94906dfd45fbaf5e524e3ad428eb2023-11-20T08:23:24ZengMDPI AGSensors1424-82202020-07-012015422410.3390/s20154224A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output EnergyGuillermo Almonacid-Olleros0Gabino Almonacid1Juan Ignacio Fernandez-Carrasco2Macarena Espinilla-Estevez3Javier Medina-Quero4Department of Electronic Engineering, Campus Las Lagunillas, 23071 Jaén, SpainDepartment of Electronic Engineering, Campus Las Lagunillas, 23071 Jaén, SpainDepartment of Electronic Engineering, Campus Las Lagunillas, 23071 Jaén, SpainDepartment of Computer Science, Campus Las Lagunillas, 23071 Jaén, SpainDepartment of Computer Science, Campus Las Lagunillas, 23071 Jaén, SpainThe classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance.https://www.mdpi.com/1424-8220/20/15/4224photovoltaic systemsnowcasting energy generationtemporal windows |
| spellingShingle | Guillermo Almonacid-Olleros Gabino Almonacid Juan Ignacio Fernandez-Carrasco Macarena Espinilla-Estevez Javier Medina-Quero A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy Sensors photovoltaic systems nowcasting energy generation temporal windows |
| title | A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy |
| title_full | A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy |
| title_fullStr | A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy |
| title_full_unstemmed | A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy |
| title_short | A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy |
| title_sort | new architecture based on iot and machine learning paradigms in photovoltaic systems to nowcast output energy |
| topic | photovoltaic systems nowcasting energy generation temporal windows |
| url | https://www.mdpi.com/1424-8220/20/15/4224 |
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