Оригинал (Original)
Автори: Георгиев, М. Г., Георгиева, А. Н., Николова, Д. Н., Миланов, К. Г.
Заглавие: Neural Networks Application for Modelling of RES production
Ключови думи: neural networks , forecasting , solar energy , photovoltaic system , electricity production

Абстракт: The article is focused on modeling and analyses of electricity production from renewable energy sources in a single-family house using artificial intelligence. The neural networks are one of the main instruments for modeling and forecasting of dynamic and stochastic processes. In the current research, they are implemented to modeling of the electricity production of a photovoltaic station of a single-family house with the aim to be able to analyze and predict the PV production in short and long term periods. This is very important in the current electricity systems in respect to ensure optimal energy utilization in the house and optimal facilities exploitation. In the article real data from an existing SCADA are used which makes the results close to real exploitation.

Библиография

  1. [2] Olawoyin,A., Chen,Y., 2018, Predicting the Future with Artificial Neural Network, Procedia Comput. Sci, том 140, стр. стр. 383-392

Издание

2021 13th Electrical Engineering Faculty Conference (BulEF), том 1, брой 1, стр. стр. 1-6, 2021, България, Varna, IEEE Xplore, DOI 10.1109/BulEF53491.2021.9690840

Издателските права се държат от IEEE

Пълен текст на публикацията

Autors: Georgiev, M. G., Georgieva, A. G., Gospodinova, D. G., Milanov, K. M.
Title: Neural Networks Application for Modelling of RES production
Keywords: neural networks , forecasting , solar energy , photovoltaic system , electricity production

Abstract: The article is focused on modeling and analyses of electricity production from renewable energy sources in a single-family house using artificial intelligence. The neural networks are one of the main instruments for modeling and forecasting of dynamic and stochastic processes. In the current research, they are implemented to modeling of the electricity production of a photovoltaic station of a single-family house with the aim to be able to analyze and predict the PV production in short and long term periods. This is very important in the current electricity systems in respect to ensure optimal energy utilization in the house and optimal facilities exploitation. In the article real data from an existing SCADA are used which makes the results close to real exploitation.

References

  1. [2] Olawoyin,A., Chen,Y., 2018, Predicting the Future with Artificial Neural Network, Procedia Comput. Sci, том 140, стр. стр. 383-392

Issue

2021 13th Electrical Engineering Faculty Conference (BulEF), vol. 1, issue 1, pp. 1-6, 2021, Bulgaria, Varna, IEEE Xplore, DOI 10.1109/BulEF53491.2021.9690840

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Full text of the publication

Цитирания (Citation/s):
1. Sahu, A. R., Bose, B., Kumar, S., & Tayal, V. K. (2020, June). A Review of Various Power Management Schemesin HEV. In 2020 8th international conference on reliability, infocom technologies and optimization (trends and future directions)(ICRITO) (pp. 1296-1300). IEEE. - 2020 - в издания, индексирани в Scopus или Web of Science

Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus