太阳能发电预测的关键问题和未来趋势

Yuan-Kang Wu, Cheng-Liang Huang
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摘要

由于云层的影响,太阳辐照度是间歇性的,给电力系统运行带来不确定性。因此,准确的太阳能发电预测有助于系统运营商降低发电机组调度的风险和成本。与风力发电预测相比,太阳能发电预测具有更高的挑战性,因为太阳辐照度的预测比较困难。然而,太阳辐照度曲线包含一个每日的模式。因此,统计方法或深度学习模型捕捉到了太阳能发电的重要特征,从而提高了预测的准确性。本文总结了太阳能发电预报的关键问题,包括潜力训练模型、数据处理、高效天气分类以及其他相关技术。此外,还讨论了太阳能预测的可能趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Key Issues and Future Trends on Solar Power Forecasting
Owing to the effect of cloud, solar irradiance is intermittent and causes uncertainty in power system operations. Thus, accurate solar power forecasting helps system operators reduce the risk and cost of generator scheduling. Compared to wind power forecasting, solar power forecasting has a higher challenge because the prediction of solar irradiance is difficult. However, the curve of solar irradiance includes a daily pattern. Thus, statistical methods or deep learning models capture the important characteristics of solar power generation, which improves forecasting accuracy. This study summarizes essential issues about solar power forecasting, including potential training models, data processing, efficient weather classification, and other corresponding technologies. In addition, possible trends for solar power forecasts are also addressed.
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