K.-K. Drakaki, G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data, Advances in Geosciences, 56, 155–162, doi:10.5194/adgeo-56-155-2022, 2022.
[Ημερήσια παραγωγή ενέργειας σε μικρά υδροηλεκτρικά έργα: πρόγνωσεις υπό αβεβαιότητα μέσω αποτελεσματικής σύζευξης γνώσης και δεδομένων]
[doc_id=2165]
[Αγγλικά]
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Βλέπε επίσης: https://adgeo.copernicus.org/articles/56/155/2022/
Σημείωση:
Τα μοντέλα προσομοίωσης και πρόγνωσης αναπτύχθηκαν σε περιβάλλον R environment και είναι διαθέσιμα στην ακόλουθη διεύθυνση: https://github.com/corinadrakaki/Day-ahead-energy-production-in-small-hydropower-plants
Εργασίες μας στις οποίες αναφέρεται αυτή η εργασία:
1. | A. Efstratiadis, A. Tegos, A. Varveris, and D. Koutsoyiannis, Assessment of environmental flows under limited data availability – Case study of the Acheloos River, Greece, Hydrological Sciences Journal, 59 (3-4), 731–750, doi:10.1080/02626667.2013.804625, 2014. |
2. | A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Hydrological modelling of temporally-varying catchments: Facets of change and the value of information, Hydrological Sciences Journal, 60 (7-8), 1438–1461, doi:10.1080/02626667.2014.982123, 2015. |
3. | I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Stochastic periodic autoregressive to anything (SPARTA): Modelling and simulation of cyclostationary processes with arbitrary marginal distributions, Water Resources Research, 54 (1), 161–185, WRCR23047, doi:10.1002/2017WR021394, 2018. |
4. | I. Tsoukalas, Modelling and simulation of non-Gaussian stochastic processes for optimization of water-systems under uncertainty, PhD thesis, 339 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Δεκέμβριος 2018. |
5. | P. Kossieris, I. Tsoukalas, C. Makropoulos, and D. Savic, Simulating marginal and dependence behaviour of water demand processes at any fine time scale, Water, 11 (5), 885, doi:10.3390/w11050885, 2019. |
6. | G. Papacharalampous, H. Tyralis, A. Langousis, A. W. Jayawardena, B. Sivakumar, N. Mamassis, A. Montanari, and D. Koutsoyiannis, Probabilistic hydrological post-processing at scale: Why and how to apply machine-learning quantile regression algorithms, Water, doi:10.3390/w11102126, 2019. |
7. | I. Tsoukalas, P. Kossieris, and C. Makropoulos, Simulation of non-Gaussian correlated random variables, stochastic processes and random fields: Introducing the anySim R-Package for environmental applications and beyond, Water, 12 (6), 1645, doi:10.3390/w12061645, 2020. |
8. | G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, A dilemma of small hydropower plants: Design with uncertainty or uncertainty within design?, EGU General Assembly 2021, online, EGU21-2398, doi:10.5194/egusphere-egu21-2398, European Geosciences Union, 2021. |
9. | G.-K. Sakki, I. Tsoukalas, and A. Efstratiadis, A reverse engineering approach across small hydropower plants: a hidden treasure of hydrological data?, Hydrological Sciences Journal, 67 (1), 94–106, doi:10.1080/02626667.2021.2000992, 2022. |
Κατηγορίες: Υδροπληροφορική, Ανανεώσιμες πηγές ενέργειας, Εργασίες φοιτητών σε συνέδρια, Νερό και ενέργεια