Predictive evaluation of solar energy variables for a large-scale solar power plant based on triple deep learning forecast models-Reference-Cited by-同舟云学术

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Predictive evaluation of solar energy variables for a large-scale solar power plant based on triple deep learning forecast models

Published:2023-08 Issue: Volume:76 Page:51-73
ISSN:1110-0168
Container-title:Alexandria Engineering Journal
language:en
Short-container-title:Alexandria Engineering Journal

Author:

Jamil Irfan,Lucheng Hong,Iqbal Sheeraz,Aurangzaib Muhammad,Jamil Rehan^ORCID,Kotb Hossam^ORCID,Alkuhayli Abdulaziz,AboRas Kareem M.^ORCID

Publisher

Elsevier BV

Subject

General Engineering

Reference79 articles.

1. F. Aksan, Y. Li, V. Suresh, P. Janik, CNN-LSTM vs. LSTM-CNN to predict power flow direction: a case study of the high-voltage subnet of Northeast Germany. Sensors (Basel) 23(2) (2023 Jan 12) 901, 10.3390/s23020901 (PMID: 36679696; PMCID: PMC9864294).

2. Completed review of various solar power forecasting techniques considering different viewpoints;Wu;Energies,2022

3. Machine learning for solar irradiance forecasting of photovoltaic system;Li;Renew. Energy,2016

4. Jui-ShengChou, Duc-SonTran. Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy 165(Part B) (15 December 2018) 709–726.

5. Forecast of solar energy production - a deep learning approach;Zhang;IEEE Int. Conf. Big Knowledge (ICBK),2018

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