Abstract
Earlier in [1], it was concluded that it is necessary to improve the learning algorithms of neural networks operating in systems that generate electricity using renewable energy sources. This article is intended to acquaint the reader with a new type of activation functions of artificial neural networks (ANN), namely - the use of Legendre polynomials, as well as a new method of learning ANN, when this process is not sequential, as usual, but in parallel. The accepted statements made it possible to make sure that the new, designed neural network has better properties (such as training time and less value of learning error) than the standard ones. The relevance of this topic lies in the following provisions: - improving the interaction between the solar station and artificial intelligence systems, through increased productivity; - taking into account the transients in the electrical network by means of intelligent control, through the use of neural networks of the proposed architecture. The developed neural networks have found their application in the work of a photovoltaic station. Their main purpose is to fulfill the forecast in the electrical networks of the amount of generated power. To successfully complete the task, the following goals were set and solved: to analyze and compare standard activation functions and algorithms for ANN training, to show methods and describe the improvement of networks, to demonstrate the application of developed ANN in photovoltaic problems. This article was designed to acquaint with the new method of building neural networks, which is based on seeing the transmission of signals in a non-sequential way, such as parallel, with certain features of the connection with which it was given in the text. The paper also demonstrates the use of the Legendre polynomial using qualitative neural network activation functions that work with solar panels. For confirmation in the article the answers to calculations are given. In future materials it is planned to streamline in more detail the process of modeling and compiling a mathematical calculation for the construction of neural networks.
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