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This work entails producing load forecasting through LSTM and LSTM ensembled networks and put up a comparative picture between the two. Our work establishes that LSTM ensemble learning can produce a better prediction compared to single LSTM networks.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8220c2fe8bf416877b748eb422d9f733
https://doi.org/10.36227/techrxiv.16807213.v3
https://doi.org/10.36227/techrxiv.16807213.v3
This work entails producing load forecasting through lstm and lstm ensembled networks and put up a comparative picture between the two. Our work establishes that lstm ensemble learning can produce a better prediction compared to single lstm networks.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::460ec6ae3b7801214c945ec7971242e0
https://doi.org/10.36227/techrxiv.16807213.v1
https://doi.org/10.36227/techrxiv.16807213.v1
Autor:
Md. Shahinur Rahman, Md. Selim Hossain, Enamul Hasan Rahat, Debopriya Roy Dipta, Fathun Karim Fattah, Hossain Mansur Resalat Faruque
Publikováno v:
2019 International Conference on Electrical, Computer and Communication Engineering (ECCE).
Exchange of private information over the public domain is very much susceptible to unauthorized access, therefore this necessitates the need for a cryptosystem to ensure the protection of information against forthcoming threats. Elliptical curve cryp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c25d986c45fee121c440cf0337d0e779
https://doi.org/10.1109/ecace.2019.8679184
https://doi.org/10.1109/ecace.2019.8679184
