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. Motivated by the challenges induced by the so-called Target Model and theassociated changes to the current structure of the energy market, we revisitthe problem of day-ahead prediction of power production from SmallHydropower Plants (SHPPs) without storage capacity. Using as an example atypical run-of-river SHPP in Western Greece, we test alternative forecastingschemes (from regression-based to machine learning) that take advantage ofdifferent levels of information. In this respect, we investigate whether itis preferable to use as predictor the known energy production of previousdays, or to predict the day-ahead inflows and next estimate the resultingenergy production via simulation. Our analyses indicate that the secondapproach becomes clearly more advantageous when the expert's knowledge aboutthe hydrological regime and the technical characteristics of the SHPP isincorporated within the model training procedure. Beyond these, we alsofocus on the predictive uncertainty that characterize such forecasts, withoverarching objective to move beyond the standard, yet risky, pointforecasting methods, providing a single expected value of power production.Finally, we discuss the use of the proposed forecasting procedure underuncertainty in the real-world electricity market.

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