Predictive model of power generation using Long Short Term Memory on hydrological data

The level of Small Hydro Power (SHP) development in Nigeria is spreading over many river basins and also various unexploited SHP sites discovered which can contribute to the power generation of the nation. These SHP potential sites can be estimated to a capacity of over 3000MW if utilized. Hydrological data are analysed from some rivers to know their potential on what they can generate to provide the rural populace for industrialization, employment generation and development. Hence, an estimate of power generation is done via Long Short-Term Memory predictive model using Python, Keras framework, and Tensor Flow backend as tools.