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Recurrent network based planning and management of PV based islanded microgrid

Solar energy is an intermittent source and purely Photo-voltaic (PV) based, or PV and storage based microgrids require characterization and modelling of PV resources for an effective planning and effective operations. In this research work long-short term memory (LSTM) as a recurrent neural network model is created for forecasting the PV solar resources, in which can assist in quantifying PV generation in various time intervals (hourly, daily, weekly). PV based microgrids often experience expensive or inaccurate resources planning due to the lack of accurate forecasting tools. The proposed LSTM model is simulated based on a real-time basis and the results are analyzed for its impact on planning and operations, and compared with conventional models such as Support Vector Machines - Regression (SVR). Hence, this model can be integrated further with existing energy management (demand side) and monitoring systems to streamline microgrid operations in its entirety.