Abstract

In this paper, the octonion neural network is investigated to forecast the short-term solar irradiance. The previous and the next eight values solar irradiance are organized into two octonion values; thereby the network could be constructed. This method not just gives the opportunity to forecast eight values ahead solar irradiance using one octonion input but also takes all the advantages of the octonion domain. The octonion input contains the past values solar irradiance which produces dynamics naturally to the network and decreases the input dimension vector. The octonion training algorithm has eight dimensions rather than one dimension in the real-valued neural networks. Comparison with the real-valued neural networks for forecasting solar irradiance shows that the proposed method is promising to deal with such problem. The optimal structure is used to manage the an autonomous photovoltaic (PV) system that contains the PV modules and the battery bank. The use of the proposed method presents benefits for the number of the used modules and for the battery energy requested as well.

中文翻译：

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使用八元神经网络的短期太阳辐照度预测和光伏系统管理

摘要

本文研究了八元神经网络，以预测短期太阳辐照度。太阳辐照度的前八个和后八个值被组织为两个八度值。从而可以构建网络。这种方法不仅使有机会使用一个八元输入来预测太阳辐照度之前的八个值，而且还利用了八元域的所有优势。八分音输入包含过去值的太阳辐照度，该值自然会为网络产生动力并减小输入尺寸矢量。在实值神经网络中，八张调律训练算法具有八个维度，而不是一个维度。与实值神经网络进行太阳辐照度预测的比较表明，该方法有望解决这一问题。最佳结构用于管理包含光伏模块和电池组的自主光伏（PV）系统。所提出的方法的使用对于所使用的模块的数量以及所请求的电池能量也带来益处。