Robust time series analysis for forecasting photovoltaic energy yield
This study introduces an approach to forecasting the power output of a photovoltaic (PV) system by employing an ARIMA-based algorithm. Two distinct ARIMA models were designed – one generated via SPSS and one selected by the researchers. Their effectiveness is gauged using various goodness-of-fit met...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/38/e3sconf_eepes2025_02003.pdf |
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| author | Sapundzhi Fatima Chikalov Aleksandar Georgiev Slavi Georgiev Ivan Todorov Venelin |
| author_facet | Sapundzhi Fatima Chikalov Aleksandar Georgiev Slavi Georgiev Ivan Todorov Venelin |
| author_sort | Sapundzhi Fatima |
| collection | DOAJ |
| description | This study introduces an approach to forecasting the power output of a photovoltaic (PV) system by employing an ARIMA-based algorithm. Two distinct ARIMA models were designed – one generated via SPSS and one selected by the researchers. Their effectiveness is gauged using various goodness-of-fit metrics, which provide a detailed evaluation of each model’s precision. In addition, the autocorrelation (ACF) and partial autocorrelation (PACF) functions of the residuals are analysed to confirm the models’ soundness, while confidence intervals for these residuals are calculated to further substantiate their validity. The analysis proceeds with the generation of monthly predictions for the dataset, complete with their own confidence bounds, thereby showcasing the forecasting strength of the models. The findings underscore the utility of ARIMA techniques in projecting PV energy yields, delivering critical insights that can be leveraged to enhance system performance and strategic planning. Overall, this work aims to contribute to renewable energy forecasting by demonstrating that ARIMA models are a viable tool for predicting the monthly operational outcomes of photovoltaic systems. |
| format | Article |
| id | doaj-art-3e1466d13a0f4190b2e48437c8ca19e8 |
| institution | DOAJ |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-3e1466d13a0f4190b2e48437c8ca19e82025-08-20T02:40:56ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016380200310.1051/e3sconf/202563802003e3sconf_eepes2025_02003Robust time series analysis for forecasting photovoltaic energy yieldSapundzhi Fatima0Chikalov Aleksandar1Georgiev Slavi2Georgiev Ivan3Todorov Venelin4South-West University “Neofit Rilski”, Department of Communication and Computer Engineering, Faculty of EngineeringSouth-West University “Neofit Rilski”, Department of Communication and Computer Engineering, Faculty of EngineeringBulgarian Academy of Sciences, Institute of Mathematics and Informatics, Department of Information ModelingBulgarian Academy of Sciences, Institute of Mathematics and Informatics, Department of Information ModelingBulgarian Academy of Sciences, Institute of Information and Communication Technologies, Department of Parallel Algorithms and Machine Learning with Neurotechnology LaboratoryThis study introduces an approach to forecasting the power output of a photovoltaic (PV) system by employing an ARIMA-based algorithm. Two distinct ARIMA models were designed – one generated via SPSS and one selected by the researchers. Their effectiveness is gauged using various goodness-of-fit metrics, which provide a detailed evaluation of each model’s precision. In addition, the autocorrelation (ACF) and partial autocorrelation (PACF) functions of the residuals are analysed to confirm the models’ soundness, while confidence intervals for these residuals are calculated to further substantiate their validity. The analysis proceeds with the generation of monthly predictions for the dataset, complete with their own confidence bounds, thereby showcasing the forecasting strength of the models. The findings underscore the utility of ARIMA techniques in projecting PV energy yields, delivering critical insights that can be leveraged to enhance system performance and strategic planning. Overall, this work aims to contribute to renewable energy forecasting by demonstrating that ARIMA models are a viable tool for predicting the monthly operational outcomes of photovoltaic systems.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/38/e3sconf_eepes2025_02003.pdf |
| spellingShingle | Sapundzhi Fatima Chikalov Aleksandar Georgiev Slavi Georgiev Ivan Todorov Venelin Robust time series analysis for forecasting photovoltaic energy yield E3S Web of Conferences |
| title | Robust time series analysis for forecasting photovoltaic energy yield |
| title_full | Robust time series analysis for forecasting photovoltaic energy yield |
| title_fullStr | Robust time series analysis for forecasting photovoltaic energy yield |
| title_full_unstemmed | Robust time series analysis for forecasting photovoltaic energy yield |
| title_short | Robust time series analysis for forecasting photovoltaic energy yield |
| title_sort | robust time series analysis for forecasting photovoltaic energy yield |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/38/e3sconf_eepes2025_02003.pdf |
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