Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant

Nonthawat Khortsriwong, Promphak Boonraksa, Terapong Boonraksa, Thipwan Fangsuwannarak, Asada Boonsrirat, Watcharakorn Pinthurat and Boonruang Marungsri ()
Additional contact information
Nonthawat Khortsriwong: School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Promphak Boonraksa: School of Electrical Engineering, Rajamangala University of Technology Suvarnabhumi, Nonthaburi 11000, Thailand
Terapong Boonraksa: School of Electrical Engineering, Rajamangala University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand
Thipwan Fangsuwannarak: School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Asada Boonsrirat: Energy Solution Business, SCG Chemicals Public Co., Ltd., Bangsue, Bangkok 10800, Thailand
Watcharakorn Pinthurat: School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia
Boonruang Marungsri: School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand

Energies, 2023, vol. 16, issue 5, 1-21

Abstract: Recently, deep learning techniques have become popular and are widely employed in several research areas, such as optimization, pattern recognition, object identification, and forecasting, due to the advanced development of computer programming technologies. A significant number of renewable energy sources (RESs) as environmentally friendly sources, especially solar photovoltaic (PV) sources, have been integrated into modern power systems. However, the PV source is highly fluctuating and difficult to predict accurately for short-term PV output power generation, leading to ineffective system planning and affecting energy security. Compared to conventional predictive approaches, such as linear regression, predictive-based deep learning methods are promising in predicting short-term PV power generation with high accuracy. This paper investigates the performance of several well-known deep learning techniques to forecast short-term PV power generation in the real-site floating PV power plant of 1.5 MWp capacity at Suranaree University of Technology Hospital, Thailand. The considered deep learning techniques include single models (RNN, CNN, LSTM, GRU, BiLSTM, and BiGRU) and hybrid models (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Five-minute resolution data from the real floating PV power plant is used to train and test the deep learning models. Accuracy indices of MAE, MAPE, and RMSE are applied to quantify errors between actual and forecasted values obtained from the different deep learning techniques. The obtained results show that, with the same training dataset, the performance of the deep learning models differs when testing under different weather conditions and time horizons. The CNN-BiGRU model offers the best performance for one-day PV forecasting, while the BiLSTM model is the most preferable for one-week PV forecasting.

Keywords: floating PV power plant; deep learning techniques; short-term PV power forecasting; PV generation; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/5/2119/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/5/2119/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:5:p:2119-:d:1076656

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().