Solar Power Forecasting for One Day by Using LSTM Method via Iot

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A. L. Chockalingam, V. Mouleeswaran, M. S. Kavinbharathi, V. Krishnamoorthy

Abstract

In generally the weather fоreсаsting system was not done the LSTM neural networks for sоlаr роwer fоreсаsting. The neural networks for short-duration of photovoltaic solar power forecasting had been matured and got the result. In that single-step and multi-step photovoltaic, forecasting was presented and their data were analyzed deeply. The new advanced neural network algorithm where lead to the acceptable values in data accuracy in the cloudy days. Distributed energy resources (DER) caused substantial impact on the operation of incorporate solar power generation plant and the dispatched unit. The multi- step[7] solar power forecasting of PV power remains an opened challenged. Moreover, the forecasting of the LSTM model could've successfully captured the intra-day generated power on different weather conditions. In this, all the weather data were sensed by the specified sensors and transfers data collected from the PV generating system were stored in the cloud through the IoT platform. The stored data in the cloud would be used for future PV-related researched worked or projects. All the data in the cloud would be got a report in excel format instantly.

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How to Cite
A. L. Chockalingam, V. Mouleeswaran, M. S. Kavinbharathi, V. Krishnamoorthy. (2021). Solar Power Forecasting for One Day by Using LSTM Method via Iot. Annals of the Romanian Society for Cell Biology, 25(6), 4336 –. Retrieved from https://annalsofrscb.ro/index.php/journal/article/view/6267
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