Abstract. Motivated by the challenges induced by the so-called Target Model and the
associated changes to the current structure of the energy market, we revisit
the problem of day-ahead prediction of power production from Small
Hydropower Plants (SHPPs) without storage capacity. Using as an example a
typical run-of-river SHPP in Western Greece, we test alternative forecasting
schemes (from regression-based to machine learning) that take advantage of
different levels of information. In this respect, we investigate whether it
is preferable to use as predictor the known energy production of previous
days, or to predict the day-ahead inflows and next estimate the resulting
energy production via simulation. Our analyses indicate that the second
approach becomes clearly more advantageous when the expert's knowledge about
the hydrological regime and the technical characteristics of the SHPP is
incorporated within the model training procedure. Beyond these, we also
focus on the predictive uncertainty that characterize such forecasts, with
overarching objective to move beyond the standard, yet risky, point
forecasting methods, providing a single expected value of power production.
Finally, we discuss the use of the proposed forecasting procedure under
uncertainty in the real-world electricity market.
Object oriented modelling and simulation of hydropower plants with run-of-river scheme: A new simulation tool
