PV Power Estimation is the process of predicting the amount of electricity generated by photovoltaic (PV) solar panels over a specific period. It involves analyzing factors like solar irradiance, panel orientation, temperature, shading, and efficiency to estimate energy output. Accurate PV power estimation helps optimize the design, operation, and maintenance of solar power systems, ensuring efficient energy production and integration into the grid. It is crucial for energy planning, cost analysis, and renewable energy integration.

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Data-Driven Day-Ahead PV Estimation Using Hybrid Deep Learning

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Ongoing smart grid activities and associated automation resulted in rich set of data. These data can be utilized for monitoring and estimation of real time photovoltaic (PV) generation. Inherent variability in PV and related impact on power systems is a challenging problem. Improving the accuracy of PV generation estimation is beneficial for both the PV owners and the grid operators. Recently, deep learning algorithms possible by the availability of data have shown its advantages for time series estimation; however, its application on PV generation estimation is still in the early stage. In this paper, a hybrid estimation model with a combination of long-short-term-memory network (LSTM) and persistence model (PM) is developed to provide day-ahead PV estimation at 15-minute time interval with high accuracy and robustness. Simulation results show the superior performance of the proposed method over existing methods for most of the test c

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