A Recent Invasion Wave Of Deep Learning In Solar Power Forecasting Techniques Using Ann

Mapping Intimacies ◽

10.1109/ifeec53238.2021.9661747 ◽

2021 ◽

Author(s):

Tuan-Anh Nguyen ◽

Manh-Hai Pham ◽

Trung-Kien Duong ◽

Minh-Phap Vu

Keyword(s):

Deep Learning ◽

Solar Power ◽

Invasion Wave ◽

Solar Power Forecasting ◽

Power Forecasting ◽

Forecasting Techniques

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Benchmark analysis of day-ahead solar power forecasting techniques using weather predictions

2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) ◽

10.1109/pvsc40753.2019.8980899 ◽

2019 ◽

Author(s):

Lennard Visser ◽

Tarek AlSkaif ◽

Wilfried van Sark

Keyword(s):

Solar Power ◽

Benchmark Analysis ◽

Solar Power Forecasting ◽

Power Forecasting ◽

Forecasting Techniques

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A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2020.109792 ◽

2020 ◽

Vol 124 ◽

pp. 109792 ◽

Author(s):

R. Ahmed ◽

V. Sreeram ◽

Y. Mishra ◽

M.D. Arif

Keyword(s):

Solar Power ◽

State Of The Art ◽

The State ◽

Solar Power Forecasting ◽

Power Forecasting ◽

Forecasting Techniques

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Big data solar power forecasting based on deep learning and multiple data sources

Expert Systems ◽

10.1111/exsy.12394 ◽

2019 ◽

Vol 36 (4) ◽

Author(s):

José F. Torres ◽

Alicia Troncoso ◽

Irena Koprinska ◽

Zheng Wang ◽

Francisco Martínez‐Álvarez

Keyword(s):

Big Data ◽

Deep Learning ◽

Solar Power ◽

Data Sources ◽

Multiple Data Sources ◽

Multiple Data ◽

Solar Power Forecasting ◽

Power Forecasting

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Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc.2016.7844673 ◽

2016 ◽

Author(s):

Andre Gensler ◽

Janosch Henze ◽

Bernhard Sick ◽

Nils Raabe

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Solar Power ◽

Solar Power Forecasting ◽

Power Forecasting

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A Robust Auto Encoder-Gated Recurrent Unit (AE-GRU) Based Deep Learning Approach for Short Term Solar Power Forecasting

Optik ◽

10.1016/j.ijleo.2021.168515 ◽

2021 ◽

pp. 168515

Author(s):

Amit Rai ◽

Ashish Shrivastava ◽

Kartick C Jana

Keyword(s):

Deep Learning ◽

Solar Power ◽

Learning Approach ◽

Solar Power Forecasting ◽

Gated Recurrent Unit ◽

Power Forecasting

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The Latest Update of JMA Numerical Weather Prediction Models and its Solar Power Forecasting Errors

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes.138.881 ◽

2018 ◽

Vol 138 (11) ◽

pp. 881-892 ◽

Author(s):

Hideaki Ohtake ◽

Fumichika Uno ◽

Takashi Oozeki ◽

Yoshinori Yamada

Keyword(s):

Numerical Weather Prediction ◽

Solar Power ◽

Prediction Models ◽

Weather Prediction ◽

Numerical Weather ◽

Solar Power Forecasting ◽

Forecasting Errors ◽

Numerical Weather Prediction Models ◽

Power Forecasting

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Short Term Solar Power Forecasting using Hybrid Minimum Variance Expanded RVFLN and Sine-Cosine Levy Flight PSO Algorithm

Renewable Energy ◽

10.1016/j.renene.2021.04.088 ◽

2021 ◽

Author(s):

Deepak Ranjan Das ◽

P.K. Dash ◽

Ranjeeta Bisoi

Keyword(s):

Solar Power ◽

Pso Algorithm ◽

Minimum Variance ◽

Lévy Flight ◽

Levy Flight ◽

Solar Power Forecasting ◽

Power Forecasting

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Multi-channel convolutional neural network for integration of meteorological and geographical features in solar power forecasting

Applied Energy ◽

10.1016/j.apenergy.2021.117083 ◽

2021 ◽

Vol 295 ◽

pp. 117083

Author(s):

Jae Heo ◽

Kwonsik Song ◽

SangUk Han ◽

Dong-Eun Lee

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Solar Power ◽

Solar Power Forecasting ◽

Power Forecasting

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Post-processing of NWP forecasts using Kalman filtering with operational constraints for day-ahead solar power forecasting in Thailand

IEEE Access ◽

10.1109/access.2021.3099481 ◽

2021 ◽

pp. 1-1

Author(s):

Supachai Suksamosorn ◽

Naebboon Hoonchareon ◽

Jitkomut Songsiri

Keyword(s):

Kalman Filtering ◽

Solar Power ◽

Post Processing ◽

Operational Constraints ◽

Solar Power Forecasting ◽

Power Forecasting

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Solar power forecasting with sparse vector autoregression structures

2017 IEEE Manchester PowerTech ◽

10.1109/ptc.2017.7981201 ◽

2017 ◽

Author(s):

Laura Cavalcante ◽

Ricardo J. Bessa

Keyword(s):

Solar Power ◽

Vector Autoregression ◽

Sparse Vector ◽

Solar Power Forecasting ◽

Power Forecasting

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