Abstract:
Among the many advantages of water, is its use for power generation and since a dam
is expensive to construct, the run-of-river storage is considered for predicting suitable
location of Small Hydro Power turbines installation. There is also the challenge of
non-availability of hydrological data for some of the rivers in Nigeria for national
planning and statistics. This research work applied one of the deep learning
algorithms; Long Short-Term Memory (LSTM) networks to develop a predictive
model from the historical data of the Benin-Owena River Basin Development
Authority. The predictive model was implemented as a web-based application with
Hypertext Processor and Laravel as the framework with Hypertext Markup Language,
Cascading Style Sheet, Bootstrap, JQuery, and JavaScript for the front end side while
Python 3 with Tensorflow as the backend engine alongside Keras packages. The
LSTM predictive model was trained with seventeen river datasets and the results
indicated appropriate turbines that can be installed in each river from the prediction
and power generation estimate. The LSTM predictive model used a head of 16.95m,
discharge of 22.10 m3/s from for Ethiope River and resulted in power output of
32.92kw.Also, the Root Mean Square Error used for performance evaluation gave a
value of 17.29 for Ethiope River water height and 3.61 for discharge. The overall
energy delivered was high and the recommended turbines were crossflow, propeller or
kaplan. Therefore, the results showed that LSTM model resulted in a higher power
output generation.
