Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future
Renewables are the greener substitute for the conventional polluting sources of generating energy. For their successful integration into the power grid, accurate forecasts are required. In this paper, we report the lessons acquired from our previous works on generating time-series ARIMA-based foreca...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/16/6005 |
| _version_ | 1827729881341886464 |
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| author | Robert Basmadjian Amirhossein Shaafieyoun |
| author_facet | Robert Basmadjian Amirhossein Shaafieyoun |
| author_sort | Robert Basmadjian |
| collection | DOAJ |
| description | Renewables are the greener substitute for the conventional polluting sources of generating energy. For their successful integration into the power grid, accurate forecasts are required. In this paper, we report the lessons acquired from our previous works on generating time-series ARIMA-based forecasting models for renewables. To this end, we considered a consistent dataset spanning the last four years. Assuming four different performance metrics for each of the best ARIMA-based models of our previous works, we derived a new optimal model for each month of the year, as well as for the two different methodologies suggested in those works. We then evaluated the performance of those models, by comparing the two methodologies: in doing so, we proposed a hybrid methodology that took the best models out of those two methodologies. We show that our proposed hybrid methodology has improved yearly accuracy of about 89.5% averaged over 12 months of the year. Also, we illustrate in detail for the four years under study and each month of the year the observed percentage of renewables and its corresponding accuracy compared to the generated forecasts. Finally, we give the implementation details of our open-source REN4KAST software platform, which provides several services related to renewables in Germany. |
| first_indexed | 2024-03-10T23:58:23Z |
| format | Article |
| id | doaj.art-7fbf16498d644a90b15f234db164ab36 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T23:58:23Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7fbf16498d644a90b15f234db164ab362023-11-19T00:57:23ZengMDPI AGEnergies1996-10732023-08-011616600510.3390/en16166005Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the FutureRobert Basmadjian0Amirhossein Shaafieyoun1Department of Informatics, Clausthal University of Technology, Julius-Albert-Str. 4, 38678 Clausthal-Zellerfeld, GermanyOne Data, Kapuzinerstraße 2c, 94032 Passau, GermanyRenewables are the greener substitute for the conventional polluting sources of generating energy. For their successful integration into the power grid, accurate forecasts are required. In this paper, we report the lessons acquired from our previous works on generating time-series ARIMA-based forecasting models for renewables. To this end, we considered a consistent dataset spanning the last four years. Assuming four different performance metrics for each of the best ARIMA-based models of our previous works, we derived a new optimal model for each month of the year, as well as for the two different methodologies suggested in those works. We then evaluated the performance of those models, by comparing the two methodologies: in doing so, we proposed a hybrid methodology that took the best models out of those two methodologies. We show that our proposed hybrid methodology has improved yearly accuracy of about 89.5% averaged over 12 months of the year. Also, we illustrate in detail for the four years under study and each month of the year the observed percentage of renewables and its corresponding accuracy compared to the generated forecasts. Finally, we give the implementation details of our open-source REN4KAST software platform, which provides several services related to renewables in Germany.https://www.mdpi.com/1996-1073/16/16/6005renewable energy sourcesauto-regressionmoving averageforecasting methodologies |
| spellingShingle | Robert Basmadjian Amirhossein Shaafieyoun Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future Energies renewable energy sources auto-regression moving average forecasting methodologies |
| title | Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future |
| title_full | Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future |
| title_fullStr | Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future |
| title_full_unstemmed | Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future |
| title_short | Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future |
| title_sort | assessing arima based forecasts for the percentage of renewables in germany insights and lessons for the future |
| topic | renewable energy sources auto-regression moving average forecasting methodologies |
| url | https://www.mdpi.com/1996-1073/16/16/6005 |
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