Case Study: Energy Production Forecast for Run-of-River Plants

The authors share information on a tool developed to assist in energy production at run-of-river hydro plants.

By Mario Arquilla and Fabio Pasut

The need to improve efficiency in power plant management is taking a leading role in the hydroelectric field. STE Energy has developed an energy production forecasting tool for run-of-river hydro plants that can be integrated within monitoring and supervision platforms.

To collect power plant data, the web-based STE-Monitor and STE-Guardian software systems were used. STE-Monitor, designed for the remote control of production facilities, continuously monitors the main operating parameters and, for this study, data considered are energy production profiles and alarms and faults lists. STE-Guardian is designed to plan maintenance activities, coordinate maintenance personnel and automatically send reports to the customer. Data considered are cataloging of plant shutdowns and the duration of plant shutdowns.

For weather data, the web application provided by the Italian Regional Agency for Environmental Prevention and Protection was used.

Development of the algorithm

Energy production of a run-of-river plant depends on the amount of precipitation and thus the available flow. But other factors – such as programmed plant shutdowns, internal faults, electrical grid disturbances and extreme weather conditions – significantly affect energy production. For these reasons, the collection of power plant operation data is not enough; data must be analyzed, reprocessed and understood.

For this solution, the potential of artificial neural networks (ANN) was exploited. An algorithm in Matlab Simulink was developed; it receives historical data relating to plant operation together with meteorological data and outputs the energy production profile. A neural network is formed by several layers: an input layer, several hidden layers composed of neurons, and an output layer.

During the “learning phase,” the weights associated with the various neurons of the hidden layers are iteratively changed, to obtain an output as close as possible to the real data used for comparison. A back-propagation approach with decreasing gradient was adopted to minimize the deviation between the expected output and simulation results.

The algorithm, used to update the coefficients of the network, is called a back-propagation algorithm for the fact that the offset recorded in correspondence of a certain piece of data is propagated backwards in the network to obtain the updating formulas of network coefficients. The output error is calculated using the mean square error approach.

Case studies

The forecast algorithm was tested on two Italian run-of-river plants, each with a bulb turbine with rated power of 1.031 MW, rated discharge of 26 m3/sec of and head of 4.2 to 5 m.

Scenario 1 – Medium-term forecast for Genivolta plant

Two years of historical data (operation and meteorological) were used, then the production trend over one year was forecast, giving as input the annual rainfall profile and programmed plant shutdowns. The results show there are significant divergences between the prediction and actual production during the year. Overall, however, an estimation of annual energy production with an error of less than 1% was obtained; this result is strongly dependent on the accuracy of the rainfall forecast.

Scenario 2 – Short-term forecast for Cassano plant

A year and a half of historical data (operation and meteorological) was used, and the production trend over a month was forecast, giving as input the monthly rainfall profile and the programmed plant shutdowns. The results show that there are small divergences between the prediction and actual production. The maximum daily error on the average power is 24% and the maximum monthly error on the energy production estimation is 6%. Again, the results strongly depend on the accuracy of rainfall forecast.


The forecast tool, called STE-Neural, has been integrated into the software tools STE Energy uses to supervise production facilities.

Mario Arquilla is president and chairman of the board and Fabio Pasut is head of the innovation and development department with STE Energy in Italy.

Editor’s Note: This is a digest of a paper delivered at HydroVision International 2014. View the complete paper at:

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