Photovoltaic power forecasting techniques: Application in Artificial Neural Networks

Open Access

Issue		E3S Web Conf. Volume 680, 2025 The 4^th International Conference on Energy and Green Computing (ICEGC’2025)


Article Number		00061
Number of page(s)		12
DOI		https://doi.org/10.1051/e3sconf/202568000061
Published online		19 December 2025

Guo ZF, Zhou KL, Zhang C, Lu XH, Chen W, Yang SL. Residential electricity consumption behavior: influencing factors, related theories and intervention strategies. Renew Sustain Energy Rev Jan 2018;81:399–412. [Google Scholar]
Mocanu E, Nguyen PH, Gibescu M, Kling WL. Deep learning for estimating building energy consumption. Sustain Energy Grids; Networks Jun 2016;6:91–9. [Google Scholar]
Ssen Z. Solar energy in progress and future research trends. Prog Energy Combust Sci 2004;30(4):367–416. [Google Scholar]
GUPTA, Manvi, et al. A review of PV power forecasting using machine learning techniques. Progress in Engineering Science, 2025, 100058. [Google Scholar]
GOR, Ravi Mahendra, MOHAN, Man. Industrial statistics and operational management–forecasting techniques. Tersedia online di http://doer. col. org/handle/123456789/5107 (24 September 2017), 2014. [Google Scholar]
Chu, Y.; Urquhart, B.; Gohari, S.; Pedro, H.; Kleissl, J.; Coimbra, C. Short-term reforecasting of power output from a 48 MWe solar PVplant. Sol. Energy 2015, 112, 68–77. [Google Scholar]
Aminzadeh, F.; De Groot, P. Neural Networks and Other Soft Computing Techniques with Applications in the Oil Industry; Eage Publications: Lancaster, OH, USA, 2006. [Google Scholar]
Hossain, M.; Ong, Z.; Ismail, Z.; Noroozi, S.; Khoo, S. Artificial neural networks for vibration based inverse parametric identifications: A review. Appl. Soft Comput. 2017, 52, 203–219. [Google Scholar]
DEB, Chirag, et al. A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 2017, 74: 902-924. [Google Scholar]
Schmidhuber, J., 2015. Deep learning in neural networks: an overview. Neural Netw. 61, 85–117. [CrossRef] [Google Scholar]
Srivastava S, Lessmann S. A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Sol Energy 2018;162:232–47. [Google Scholar]
LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521 (7553), 436–444. [CrossRef] [PubMed] [Google Scholar]
Tongyang Xu, Tianhua Xu, Izzat Darwazeh, Deep learning for interference cancellation in non-orthogonal signal based optical communication systems, in: 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama), IEEE, 2018. [Google Scholar]
CHOROWSKI, Jan; WANG, Jian; ZURADA, Jacek M. Review and performance comparison of SVM-and ELM-based classifiers. Neurocomputing, 2014, 128: 507-516. [Google Scholar]
AHMED, Razin, et al. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization.Renewable and sustainable energy reviews, 2020, 124: 109792. [Google Scholar]
Michael NE, Bansal RC, Ismail AAA, Elnady A, Hasan S. A cohesive structure of bi-directional long-short-term memory (BiLSTM)-GRU for predicting hourly solar radiation. Renew Energy 2024;222:119943. [Google Scholar]
MBUNGU, Nsilulu T., et al. Predictive control technique for solar photovoltaic power forecasting. Energy Conversion and Management: X, 2024, 24: 100768. [Google Scholar]

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