Parabolic Modeling Forecasts of Space and Time European Hydropower Production

Author:

Lincaru Cristina1,Grigorescu Adriana1234ORCID,Dincer Hasan15

Affiliation:

1. National Scientific Research Institute for Labor and Social Protection, Povernei Street 6, 010643 Bucharest, Romania

2. Department of Public Management, Faculty of Public Administration, National University of Political Studies and Public Administration, Expozitiei Boulevard, 30A, 012104 Bucharest, Romania

3. National Institute for Economic Research “Costin C. Kiritescu”, Romanian Academy, Casa Academiei Române, Calea 13 Septembrie nr. 13, 050711 Bucharest, Romania

4. Academy of Romanian Scientists, Ilfov Street 3, 050094 Bucharest, Romania

5. Department of Banking and Insurance, School of Business, Istanbul Medipol University, Goztepe, Kavacık South Campus, 34815 Istanbul, Turkey

Abstract

Renewable sources of energy production are some of the main targets today to protect the environment through reduced fossil fuel consumption and CO2 emissions. Alongside wind, solar, marine, biomass and nuclear sources, hydropower is among the oldest but still not fully explored renewable energy sources. Compared with other sources like wind and solar, hydropower is more stable and consistent, offering increased predictability. Even so, it should be analyzed considering water flow, dams capacity, climate change, irrigation, navigation, and so on. The aim of this study is to propose a forecast model of hydropower production capacity and identify long-term trends. The curve fit forecast parabolic model was applied to 33 European countries for time series data from 1990 to 2021. Space-time cube ArcGIS representation in 2D and 3D offers visualization of the prediction and model confidence rate. The quadratic trajectory fit the raw data for 14 countries, validated by visual check, and in 20 countries, validated by FMRSE 10% threshold from the maximal value. The quadratic model choice is good for forecasting future values of hydropower electric capacity in 22 countries, with accuracy confirmed by the VMRSE 10% threshold from the maximal value. Seven local outliers were identified, with only one validated as a global outlier based on the Generalized Extreme Studentized Deviate (GESD) test at a 5% maximal number of outliers and a 90% confidence level. This result achieves our objective of estimating a level with a high degree of occurrence and offering a reliable forecast of hydropower production capacity. All European countries show a growing trend in the short term, but the trends show a stagnation or decrease if policies do not consider intensive growth through new technology integration and digital adoption. Unfortunately, Europe does not have extensive growth potential compared with Asia–Pacific. Public policies must boost hybrid hydro–wind or hydro–solar systems and intensive technical solutions.

Funder

Romanian Ministry of Research, Innovation, and Digitalization

Publisher

MDPI AG

Reference80 articles.

1. Grigorescu, A., Ion, A.E., Lincaru, C., and Pirciog, S. (2021). Synergy analysis of knowledge transfer for the energy sector within the framework of sustainable development of the European countries. Energies, 15.

2. European Commission (2019). The European Green Deal, COM (2019) 640 Final, EUR-Lex-52019DC0640-EN-EUR-Lex (europa.eu), European Commission.

3. United Nations Framework Convention on Climate Change (UNFCCC) (2016). The Paris Agreement, Denmark, United Nations. parisagreement_publication.pdf (unfccc.int).

4. European Commission (2015). A Framework Strategy for a Resilient Energy Union with a Forward-Looking Climate Change Policy/* COM/2015/080 final */, European Commission. EUR-Lex-52015DC0080-EN-EUR-Lex (europa.eu).

5. Energy Institute-Statistical Review of World Energy (2023)—With Major Processing by Our World in Data (2024, March 27). “Electricity Generation from Hydropower” [Dataset]. Energy Institute, “Statistical Review of World Energy” [Original Data]. Available online: https://ourworldindata.org/grapher/hydro-consumption-by-region.

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