基于大数据神经网络的大面积太阳能净输出时间变化预测方法研究

Chikako Dozono, Shin-ichi Inage
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引用次数: 0

摘要

本文重点对大范围内光伏发电净输出的时间变化进行短期预测。由于光伏发电的输出波动不稳定,火力发电是必要的。然而,为了应对不可预测的功率波动,火电通常以空载待机模式运行,导致能源消耗浪费。为了解决这个问题,我们开发了一种新的预测方法,该方法利用神经网络对大范围的光伏发电净输出进行短期预测。该方法的关键方面是利用分布式太阳能发电本身作为目标区域内的传感器,从而能够使用从传感器导出的BIG数据来使用神经网络预测太阳能发电的未来净输出。为了加快计算,我们结合了一个自动编码器和一个解码器。我们将这一方法应用于九州北部,并进行了彻底核查。此外,我们将持久性模型与智能持久性模型进行了比较,并证明了它们作为可行解决方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of forecasting method of time variation of net solar output over wide area using grand data based neural network

This paper focuses on the short-term forecasting of the temporal variation in the net output of photovoltaic power generation across a wide area. Due to the unstable output fluctuations of photovoltaic power generation, thermal power generation is necessary. However, to handle unpredictable power fluctuations, thermal power often operates in a no-load standby mode, resulting in wasteful energy consumption. To address this issue, we have developed a novel prediction method that utilizes neural networks for short-term forecasting of the net output of photovoltaic power generation in a wide area. The key aspect of this method is the utilization of the distributed solar power generation itself as a sensor within the target area, enabling the use of BIG DATA derived from the sensor to predict future net output of solar power generation using a neural network. To expedite calculations, we have incorporated an autoencoder and a decoder. We applied this methodology to northern Kyushu and conducted thorough verification. Furthermore, we compared the persistent model with the smart persistent model and demonstrated their effectiveness as viable solutions.

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