Projecting Annual Rainfall Timeseries Using Machine Learning Techniques

Kyriakos Skarlatos, Eleni S. Bekri, Dimitrios Georgakellos, Polychronis Economou () and Sotirios Bersimis
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Kyriakos Skarlatos: Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece
Eleni S. Bekri: Department of Civil Engineering, University of Patras, 26504 Patras, Greece
Dimitrios Georgakellos: Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece
Polychronis Economou: Department of Civil Engineering, University of Patras, 26504 Patras, Greece
Sotirios Bersimis: Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece

Energies, 2023, vol. 16, issue 3, 1-20

Abstract: 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.

Keywords: hydropower; precipitation; Greece; machine learning; predictions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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