Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting
Solar photovoltaic energy has experienced significant growth in the last decade, as well as the challenges related to the intermittency of power generation inherent to this process. In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS). Two F...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/3/845 |
| _version_ | 1827660883277381632 |
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| author | Vanessa María Serrano Ardila Joylan Nunes Maciel Jorge Javier Gimenez Ledesma Oswaldo Hideo Ando Junior |
| author_facet | Vanessa María Serrano Ardila Joylan Nunes Maciel Jorge Javier Gimenez Ledesma Oswaldo Hideo Ando Junior |
| author_sort | Vanessa María Serrano Ardila |
| collection | DOAJ |
| description | Solar photovoltaic energy has experienced significant growth in the last decade, as well as the challenges related to the intermittency of power generation inherent to this process. In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS). Two FTS methods are proposed and evaluated to obtain a global horizontal irradiance (GHI) value. The first is the weighted method and the second is the fuzzy information granular method. Using the direct proportionality of the power with the GHI, the spatial smoothing process was applied, obtaining spatial irradiance on which a first-order low pass filter was applied to simulated power photovoltaic system generation. Thus, this study proposed indirect and direct forecasting of solar photovoltaic generation which was statistically evaluated and the results showed that the indirect prediction showed better performance with GHI than the power simulation. Error statistics, such as RMSE and MBE, show that the fuzzy information granular method performs better than the weighted method in GHI forecasting. |
| first_indexed | 2024-03-09T23:58:47Z |
| format | Article |
| id | doaj.art-d0252e8d98144252ba9fa08f6231e2c3 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T23:58:47Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d0252e8d98144252ba9fa08f6231e2c32023-11-23T16:20:51ZengMDPI AGEnergies1996-10732022-01-0115384510.3390/en15030845Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power ForecastingVanessa María Serrano Ardila0Joylan Nunes Maciel1Jorge Javier Gimenez Ledesma2Oswaldo Hideo Ando Junior3Latin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, BrazilLatin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, BrazilLatin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, BrazilResearch Group on Energy & Energy Sustainability (GPEnSE), Cabo de Santo Agostinho 54518-430, PE, BrazilSolar photovoltaic energy has experienced significant growth in the last decade, as well as the challenges related to the intermittency of power generation inherent to this process. In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS). Two FTS methods are proposed and evaluated to obtain a global horizontal irradiance (GHI) value. The first is the weighted method and the second is the fuzzy information granular method. Using the direct proportionality of the power with the GHI, the spatial smoothing process was applied, obtaining spatial irradiance on which a first-order low pass filter was applied to simulated power photovoltaic system generation. Thus, this study proposed indirect and direct forecasting of solar photovoltaic generation which was statistically evaluated and the results showed that the indirect prediction showed better performance with GHI than the power simulation. Error statistics, such as RMSE and MBE, show that the fuzzy information granular method performs better than the weighted method in GHI forecasting.https://www.mdpi.com/1996-1073/15/3/845fuzzy time seriesphotovoltaic energy predictionshort-term forecasting |
| spellingShingle | Vanessa María Serrano Ardila Joylan Nunes Maciel Jorge Javier Gimenez Ledesma Oswaldo Hideo Ando Junior Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting Energies fuzzy time series photovoltaic energy prediction short-term forecasting |
| title | Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting |
| title_full | Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting |
| title_fullStr | Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting |
| title_full_unstemmed | Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting |
| title_short | Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting |
| title_sort | fuzzy time series methods applied to in direct short term photovoltaic power forecasting |
| topic | fuzzy time series photovoltaic energy prediction short-term forecasting |
| url | https://www.mdpi.com/1996-1073/15/3/845 |
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