Short-Term forecasting of photovoltaic power in an isolated area of Ecuador using deep learning techniques

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In Ecuador, electricity generation is mainly covered by renewable energy sources that feed the National Interconnected System (NIS). Its economical price means that the installation of systems based on non-conventional renewable energy sources does not represent a benefit for the user. However, rural communities isolated from the NIS do not have electricity services. Due to this, isolated systems become an effective option to supply electricity to these communities. In this regard, the Galapagos Islands have unique biodiversity in the world. Since they are not connected to the NIS, their primary energy sources are based on biogas obtained from fossil fuels, with their negative consequences despite the great potential of solar resources. Hence the need to use non-conventional renewable energy sources that covers the energy demand and do not affect the biodiversity there. Photovoltaic energy forecasting is an essential step in the installation of photovoltaic systems. Prediction models based on deep learning (DL) techniques can obtain a high degree of accuracy in energy prediction tasks. For this reason, this work presents the development of long short-term memory (LSTM) and gated recurrent unit (GRU) models to predict photovoltaic energy in an isolated area of Ecuador. The results highlight the performance of both methods through the achieved short-term prediction.