论文标题
贝叶斯的概率太阳辐照度预测的方法
A Bayesian Approach to Probabilistic Solar Irradiance Forecasting
论文作者
论文摘要
太阳能发电的输出显着取决于可用的太阳辐射。因此，随着现代功率网格中PV产生的增殖，太阳辐照度的预测对于网格的正常运行至关重要。为了提高预测性能的准确性，本文讨论了对概率预测的贝叶斯治疗方法。使用从佛罗里达自动化天气网络（Fawn）获得的公开数据来证明该方法。该算法是在Python中开发的，结果将与点预测，其他概率方法和实际的现场结果进行比较。
The output of solar power generation is significantly dependent on the available solar radiation. Thus, with the proliferation of PV generation in the modern power grid, forecasting of solar irradiance is vital for proper operation of the grid. To achieve an improved accuracy in prediction performance, this paper discusses a Bayesian treatment of probabilistic forecasting. The approach is demonstrated using publicly available data obtained from the Florida Automated Weather Network (FAWN). The algorithm is developed in Python and the results are compared with point forecasts, other probabilistic methods and actual field results obtained for the period.
