Evaluation and prediction of solar radiation for energy management based on neural networks

International Conference on Innovations in Non-Destructive Testing (SibTest)

Aldoshina, O.V.^1 ; Van Tai, Dinh^1

Institute of Non-Destructive Testing, Tomsk Polytechnic University, Tomsk, Russia^1

关键词: Daily solar radiations;    Electrical networks;    Energy systems;    Evaluation and predictions;    Exogenous input;    Overall efficiency;    Photovoltaic energy;    Renewable energy source;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/881/1/012036/pdf
DOI  :  10.1088/1742-6596/881/1/012036

来源: IOP

【 摘 要 】

Currently, there is a high rate of distribution of renewable energy sources and distributed power generation based on intelligent networks; therefore, meteorological forecasts are particularly useful for planning and managing the energy system in order to increase its overall efficiency and productivity. The application of artificial neural networks (ANN) in the field of photovoltaic energy is presented in this article. Implemented in this study, two periodically repeating dynamic ANS, that are the concentration of the time delay of a neural network (CTDNN) and the non-linear autoregression of a network with exogenous inputs of the NAEI, are used in the development of a model for estimating and daily forecasting of solar radiation. ANN show good productivity, as reliable and accurate models of daily solar radiation are obtained. This allows to successfully predict the photovoltaic output power for this installation. The potential of the proposed method for controlling the energy of the electrical network is shown using the example of the application of the NAEI network for predicting the electric load.