Yu Soke, Yusuke Mori, Shinji Wakao, Hideaki Ohtake, Takahiro Takamatsu, Takashi Oozeki
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Abstract
The amount of photovoltaic (PV) introduced in Japan is increasing year by year. On the other hand, the issue with PV is that the amount of power generated fluctuates due to changes in the weather, which can disrupt the power demand and supply balance and have negative effects on the power system. To address this issue, technology that accurately predicts PV power generation is essential. Therefore, we predicted total PV power generation for the next day in the Tokyo Electric Power Company (TEPCO) area. In this paper, we proposed a method that adds normalized images of variables forecasted from Meso-Scale Model (MSM) of Japan Meteorological Agency (JMA) and the average values of weather information indicating the characteristics of the area to the input of Autoencoder, and confirmed that prediction accuracy was improved. It was also shown that prediction accuracy could be further improved by adding daytime and solar altitude data. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
基于多气象要素归一化图像数据的自编码器日前区域pv输出预测
日本引进的光伏(PV)数量逐年增加。另一方面,光伏发电的问题在于,由于天气的变化,发电量会出现波动,这可能会破坏电力供需平衡,对电力系统产生负面影响。为了解决这个问题,准确预测光伏发电的技术是必不可少的。因此,我们预测了东京电力公司(TEPCO)区域第二天的光伏发电总量。本文提出了一种将日本气象厅(JMA)中尺度模式(MSM)预报变量的归一化图像和指示该地区特征的天气信息平均值加到自编码器输入的方法,并验证了预测精度的提高。结果表明,通过增加日间和太阳高度数据,可以进一步提高预报精度。©2026日本电气工程师学会和Wiley期刊有限责任公司。
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