Iot Based Solar Power Monitoring And Prediction Using Cuckoo Optimized LSTM

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2021 by IJPTT Journal
	Volume-11 Issue-2
	Year of Publication : 2021
	Authors : Dr B.Sakthivel, R.Jeyapandiprathap, M.Jeyamurugan, Dr G.Narmadha
	DOI :  10.14445/22492615/IJPTT-V11I2P402

Citation

MLA Style: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 11.2 (2021): 6-8.

APA Style:Dr B.Sakthivel, R.Jeyapandiprathap, M.Jeyamurugan, Dr G.Narmadha(2021). Iot Based Solar Power Monitoring And Prediction Using Cuckoo Optimized LSTM. International Journal of P2P Network Trends and Technology, 11(2),6-8.

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.

References

[1] Krishnan, M., Jung, Y. M., & Yun, S. (2020). Prediction of Energy Demand in Smart Grid using Hybrid Approach. 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC).
[2] Chen, M.-R., Zeng, G.-Q., Lu, K.-D., &Weng, J. (2019). A Two-Layer Nonlinear Combination Method for Short-Term Wind Speed Prediction Based on ELM, ENN, and LSTM. IEEE Internet of Things Journal, 1–1.
[3] Guo, T., Liu, R., Yang, H., Shi, L., Li, F., Zhang, L., …Luo, F. (2017). Predict Atmosphere Electric Field Value with the LSTM Neural Network. 2017 International Conference on Computer Systems, Electronics, and Control (ICCSEC).
[4] Zhang, S., Wang, Y., Liu, M., &Bao, Z. (2018). Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM. IEEE Access, 6, 7675–7686.
[5] QuXiaoyun, Kang Xiaoning, Zhang Chao, Jiang Shuai, & Ma Xiuda. (2016). Short-term prediction of wind power based on deep Long Short-Term Memory. 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).
[6] Xu, G., & Xia, L. (2018). Short-Term Prediction of Wind Power Based on Adaptive LSTM. 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2).
[7] Chai, M., Xia, F., Hao, S., Peng, D., Cui, C., & Liu, W. (2019). PV Power Prediction Based on LSTM With Adaptive Hyperparameter Adjustment. IEEE Access, 7, 115473–115486.
[8] Joshi, A. S., Kulkarni, O., Kakandikar, G. M., &Nandedkar, V. M. (2017). Cuckoo Search Optimization- A Review. Materials Today: Proceedings, 4(8), 7262–7269.

Keywords
Solar Power Monitoring, LSTM, Prediction