![](bg4.png)
Visually, the forecast accuracy obtained in this
case study with respect to the previous one seems to
be lower, but the average AE is bigger for the first
case study, this is due to the magnitude of measured
quantities. Also, please consider that the water flow
through rivers tends to be periodic over time, while
wind speed is not and it depends on other physical
factors.
5 CONCLUSIONS
A feed-forward neural network has been applied to
forecast the future behavior of two different sets of
time series based on the measurements of renewable
energy resources, such as water flow and wind
speed.
In the first case study, the neural network was
used to estimate the future behavior of a water flow
time series. In the second case study presented, the
application of this forecast technique to the problem
of determining the future behavior of the wind speed
at a given site has been illustrated. The obtained
results in both cases show that the neural network
adequately represents the historical data contained in
the time series.
The obtained results are of great value as they
provide insight into the generation capacity that will
have a micro or mini-hydraulic plant and wind
system in the days forecasted, respectively.
ACKNOWLEDGEMENTS
The authors want to acknowledge the Universidad
Michoacana de San Nicolás de Hidalgo (UMSNH)
through the División de Estudios de Posgrado en
Ingeniería Eléctrica, and the Universidad de la
Ciénega del Estado de Michoacán de Ocampo
(UCM) for the facilities granted to carry-out this
investigation.
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