Abstract
Hydrological models are essential tools to forecast daily water resources' availability, which are used to plan the short-term electrical systems' operation. However, there is a trade-off when choosing a given model. Complex models may provide good results depending on very complicated analytical and optimization procedures beyond sophisticated data, whereas simpler models offer reasonable results with much more amenable tuning approaches. To improve the quality of simpler models this article proposes the coupling of the Soil Moisture Accounting Procedure (SMAP) hydrological model with a Deep Learning architecture based on Conv3D-LSTM. In the proposed methodology, the SMAP is first optimized to obtain general parameters of the hydrographic basin. This optimized model's output is used as input to the Conv3D-LSTM estimator to provide the final results. This gray estimator model can generate fast and accurate results. Studies whit the goal of forecast the natural flow seven days ahead are carried out for two large Brazilian hydroelectric plants to validate the method. The results obtained by the architecture are better than those obtained with decoupled techniques.
Highlights
The generation of electric energy through hydropower plants is one of the main forms of generation applied in the world
The results demonstrate that the Soil Moisture Accounting Procedure (SMAP) model is a tool with excellent capacity to model watersheds
SUMMARY AND CONCLUSION Forecasting the availability of water resources is an important study for several sectors, including hydroelectricity
Summary
The generation of electric energy through hydropower plants is one of the main forms of generation applied in the world. This source is the most used in countries that have considerable water potential, due to its low operating cost, when compared to other sources [1]. Brazil is one of the countries that has an electrical matrix composed predominantly of hydropower plants. In this case, the thermoelectric plants operate in order to complement the generation deficit, when the hydropower generation and alternative sources do not supply the demand of the electric system. It is necessary to carry out studies to ensure a better use of this source and to reduce the dependence on generation by thermoelectric plants, which have higher operating costs.
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