Autors: Stoyanov, L. S., Draganovska I. Y.
Title: Application of ANN for forecasting of PV plant output power - Case study Oryahovo
Keywords: Artificial neural network, multi-layer perceptron, forecast, PV power output

Abstract: Nowadays, the prediction of energy production of grid connected photovoltaic (PV) power plant is important for ensuring the quality and continuity of power supply. In this context, this paper analyzes different approaches for prediction of PV power output, using Artificial Neural Network (ANN). The Multi-Layer Perceptron (MLP) ANN method is widespread in the scientific literature for foresting of the solar irradiance and PV power output. The ANN is modeled using five years of solar data, measured or estimated using previous research and the measured power output at each 10 minutes. The accuracy of the optimal configuration is around 8% for the RRMSE.



    2021 17th Conference on Electrical Machines, Drives and Power Systems, ELMA 2021, 2021, Bulgaria,

    Цитирания (Citation/s):
    1. Luo, X., Zhang, D. A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs (2023) Energy, 268, art. no. 126636 - 2023 - в издания, индексирани в Scopus или Web of Science
    2. Han, W., Tang, Z., Xu, Z., Chen, M. Hybrid Model Based on EEMD, ARMA and Elman for Photovoltaic Power Prediction (2022) 2022 4th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2022, pp. 438-441. - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в национален форум с межд. уч., публикация в реферирано издание, индексирана в Scopus