Citation :

Dr B.Sakthivel, R.Jeyapandiprathap, M.Jeyamurugan, Dr G.Narmadha, "Iot Based Solar Power Monitoring And Prediction Using Cuckoo Optimized LSTM," International Journal of P2P Network Trends and Technology (IJPTT), vol. 11, no. 2, pp. 6-8, 2021. Crossref, https://doi.org/10.14445/22492615/IJPTT-V11I2P402

Abstract

In today`s world, the sun is the easiest and commercially feasible way of renewable energy. The modern electrical grid poses new difficulties due to its intermittent and variable nature. Soft computing-based energy monitoring and prediction techniques are used to handle these difficulties. In this work, IOT based solar power monitoring system is proposed with deep learning-based prediction. The proposed embedded system includes Arduino based controller ESP module to collect data in the server. Long short-term memory (LSTM)-based deep learning method for predicting energy generation of a solar. In order to achieve higher accuracy, cuckoo search-based optimization is applied to optimize LSTM parameters. The proposed e setup compared with other methods.

Keywords

Solar Power Monitoring, LSTM, Prediction

References

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