We propose a two-stage probabilistic solar power (SP) forecasting algorithm to utilize the solar irradiation (SI) observations measured from different locations. In the first stage, we predict the SI based on the numerical weather prediction (NWP) after interpolating SI observations. Since the SI on the target location is not measured, we interpolate it using the spatio-temporal Kriging technique based on the SI observed from nearby weather stations. In the second stage, we forecast the SP based on the SI predictions after training the SI and SP observations. The model is trained by observations, but it forecasts based on predictions. Furthermore, in the two-stage model, forecasting errors can propagate across stages. We overcome these problems by using probabilistic forecasting. We design distributions of SI predictions through the probabilistic graphical model. Then, we extract SI scenarios from the distributions and predict SP scenarios based on these SI scenarios. We also group the NWP with respect to its prediction time, and we subdivide these groups as subgroups with respect to weather conditions. Furthermore, we propose a changeable ensemble model, where we have different weights for each weather condition. We verify our algorithm based on the data from the Korea power exchange renewable energy forecasting competition 2019. We finished the competition in 2nd place among a few hundred participants.

中文翻译：

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基于双变量条件太阳辐照分布的概率太阳能预测


我们提出了一种两阶段概率太阳能（SP）预测算法，以利用从不同位置测量的太阳辐射（SI）观测结果。在第一阶段，我们根据内插 SI 观测值后的数值天气预报 (NWP) 来预测 SI。由于目标位置上的 SI 没有测量，我们根据从附近气象站观测到的 SI 使用时空克里金技术对其进行插值。在第二阶段，我们在训练 SI 和 SP 观测值后，根据 SI 预测来预测 SP。该模型通过观察进行训练，但根据预测进行预测。此外，在两阶段模型中，预测误差可以跨阶段传播。我们通过使用概率预测来克服这些问题。我们通过概率图形模型设计 SI 预测的分布。然后，我们从分布中提取 SI 场景，并根据这些 SI 场景预测 SP 场景。我们还根据预测时间对数值天气预报进行分组，并根据天气条件将这些组细分为子组。此外，我们提出了一个可变的集成模型，其中针对每种天气条件有不同的权重。我们根据 2019 年韩国电力交易所可再生能源预测竞赛的数据验证了我们的算法。我们在数百名参赛者中获得了第二名。