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Article

Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach

by 1,†, 2,*,†, 2,† and 3,†
1
Computer Science Department Signal, Image and Speech Laboratory (SIMPA) Laboratory, University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), El Mnaouar, BP 1505, Bir El Djir 31000, Algeria
2
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
3
Department of Electrical Engineering, University of Sharjah, Sharjah 27272, UAE
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(23), 8400; https://doi.org/10.3390/app10238400
Received: 7 October 2020 / Revised: 17 November 2020 / Accepted: 19 November 2020 / Published: 25 November 2020
The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models’ performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. View Full-Text
Keywords: photovoltaic power forecasting; data-driven; deep learning; variational autoencoders; RNN photovoltaic power forecasting; data-driven; deep learning; variational autoencoders; RNN
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MDPI and ACS Style

Dairi, A.; Harrou, F.; Sun, Y.; Khadraoui, S. Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach. Appl. Sci. 2020, 10, 8400. https://doi.org/10.3390/app10238400

AMA Style

Dairi A, Harrou F, Sun Y, Khadraoui S. Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach. Applied Sciences. 2020; 10(23):8400. https://doi.org/10.3390/app10238400

Chicago/Turabian Style

Dairi, Abdelkader, Fouzi Harrou, Ying Sun, and Sofiane Khadraoui. 2020. "Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach" Applied Sciences 10, no. 23: 8400. https://doi.org/10.3390/app10238400

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