Projecting Annual Rainfall Timeseries Using Machine Learning Techniques
Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/3/1459 |
| _version_ | 1797624675587588096 |
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| author | Kyriakos Skarlatos Eleni S. Bekri Dimitrios Georgakellos Polychronis Economou Sotirios Bersimis |
| author_facet | Kyriakos Skarlatos Eleni S. Bekri Dimitrios Georgakellos Polychronis Economou Sotirios Bersimis |
| author_sort | Kyriakos Skarlatos |
| collection | DOAJ |
| description | Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works. |
| first_indexed | 2024-03-11T09:45:56Z |
| format | Article |
| id | doaj.art-58f9862193f946fea68a98b1f243ee3c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T09:45:56Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-58f9862193f946fea68a98b1f243ee3c2023-11-16T16:37:47ZengMDPI AGEnergies1996-10732023-02-01163145910.3390/en16031459Projecting Annual Rainfall Timeseries Using Machine Learning TechniquesKyriakos Skarlatos0Eleni S. Bekri1Dimitrios Georgakellos2Polychronis Economou3Sotirios Bersimis4Department of Business Administration, University of Piraeus, 18534 Piraeus, GreeceDepartment of Civil Engineering, University of Patras, 26504 Patras, GreeceDepartment of Business Administration, University of Piraeus, 18534 Piraeus, GreeceDepartment of Civil Engineering, University of Patras, 26504 Patras, GreeceDepartment of Business Administration, University of Piraeus, 18534 Piraeus, GreeceHydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works.https://www.mdpi.com/1996-1073/16/3/1459hydropowerprecipitationGreecemachine learningpredictions |
| spellingShingle | Kyriakos Skarlatos Eleni S. Bekri Dimitrios Georgakellos Polychronis Economou Sotirios Bersimis Projecting Annual Rainfall Timeseries Using Machine Learning Techniques Energies hydropower precipitation Greece machine learning predictions |
| title | Projecting Annual Rainfall Timeseries Using Machine Learning Techniques |
| title_full | Projecting Annual Rainfall Timeseries Using Machine Learning Techniques |
| title_fullStr | Projecting Annual Rainfall Timeseries Using Machine Learning Techniques |
| title_full_unstemmed | Projecting Annual Rainfall Timeseries Using Machine Learning Techniques |
| title_short | Projecting Annual Rainfall Timeseries Using Machine Learning Techniques |
| title_sort | projecting annual rainfall timeseries using machine learning techniques |
| topic | hydropower precipitation Greece machine learning predictions |
| url | https://www.mdpi.com/1996-1073/16/3/1459 |
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