基于人工神经网络预测太阳辐射强度提高太阳能发电厂效率

O. Savchenko, O. Miroshnyk, O. Moroz, I. Trunova, Anatolii Sereda, Sergii Dudnikov, O. Kozlovskyi, R. Buinyi, S. Halko
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引用次数: 4

摘要

提出了基于人工神经网络理论的太阳能电站发电量预测方法。在STATISTICA软件包中创建了预测模型。为了提前24小时预测太阳辐射能量,采用了所谓的“时间窗”方法。为了预测太阳辐射强度,采用了水文气象站的测量数据。仿真时,输入“时间窗”宽度设为24小时,输出“时间窗”宽度设为1小时。这样,“时间窗口”移动了24次。本文的实践成果可供太阳能电站运营企业使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving the Efficiency of Solar Power Plants Based on Forecasting the Intensity of Solar Radiation Using Artificial Neural Networks
The method of forecasting of the generation of solar power plants on the basis of the theory of artificial neural networks is proposed. Creating a predictive model has been carried out in the STATISTICA software package. To predict the energy of solar radiation for 24 hours in advance, the so-called “time window” method was used. To predict the intensity of solar radiation, measurements of hydro meteorological station were used. During simulation, the width of the input “time window” was set to 24 hours, the width of the output “time window” was set to 1 hours. Thus, the “time window” was shifted 24 times. The practical results of the article are offered to the use of enterprises operating solar power plants.
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