Articles | Volume 56
14 Jan 2022
 | 14 Jan 2022

Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data

Korina-Konstantina Drakaki, Georgia-Konstantina Sakki, Ioannis Tsoukalas, Panagiotis Kossieris, and Andreas Efstratiadis

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Cited articles

Cassagnole, M., Ramos, M.-H., Zalachori, I., Thirel, G., Garçon, R., Gailhard, J., and Ouillon, T.: Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach, Hydrol. Earth Syst. Sci., 25, 1033–1052,, 2021. 
Coccia, G. and Todini, E.: Recent developments in predictive uncertainty assessment based on the model conditional processor approach, Hydrol. Earth Syst. Sci., 15, 3253–3274,, 2011. 
Croonenbroeck, C. and Stadtmann, G.: Renewable generation forecast studies – Review and good practice guidance, Renew. Sust. Energ. Rev., 108, 312–322,, 2019. 
Drakaki, K. K.: corinadrakaki/Day-ahead-energy-production-in-small-hydropower-plants: (v1.0.1), Zenodo [code],, 2022. 
Efstratiadis, A.: Daily inflow and rainfall data, Zenodo [data set],, 2022. 
Short summary
Since the problem of energy forecasting in SHPPs without storage capacity is little explored, we apply simple schemes to investigate: (a) the essential model structure and information; (b) the advantages of first using a flow forecasting model, instead of a direct energy forecasting; (c) the importance of rainfall data, as a proxy of the catchment state; (d) the training procedure and performance measure; and (e) the representation of predictive uncertainty and its practical interpretation.