两阶段残差学习方法提升太阳辐照预测的不确定性估计
Two-stage residual learning for calibrated uncertainty in solar irradiance forecasts
太阳能发电预测只能给单个数值不够用，这篇论文用两个LSTM分开预测辐照度和误差，不确定度偏差最多降56.5%，做新能源预测的可以看看。
一篇arXiv论文提出两阶段方法：先用LSTM网络预测全球水平辐照度期望值，再用第二个网络单独建模预测误差的分布，将误差叠加回均值得到预测值及不确定性。基于美国国家太阳辐射数据库的历史天气数据，用概率积分变换评估验证，显式建模误差使预测标准差更接近历史值：单独LSTM配置下偏差降低34.6%，结合高斯过程回归时降低56.5%。相比dropout方法只能估计模型贡献的不确定性，该方法直接建模总预测误差。
Two-stage residual learning for calibrated uncertainty in solar irradiance forecasts
Accurate forecasting of renewable generation requires not only point predictions but also reliable estimates of the uncertainty attached to them, particularly at extended horizons. Machine learning models are increasingly used to predict solar output, but they typically report a single value and capture the associated uncertainty poorly. The standard remedy is to train a model to predict the parameters of an assumed probability distribution directly. Here we develop an alternative two-stage method, motivated by synthetic solar signal generation, in which the forecast error is modeled separately from the expected irradiance. A first long short-term memory (LSTM) network predicts the expected global horizontal irradiance, and a second network models the distribution of the forecast error, which is added back to the mean to give both the forecast and its uncertainty. Using historical weather data from the National Solar Radiation Database and a probability integral transform evaluation, we show that explicitly modeling the error brings the predicted standard deviation markedly closer to the historical value, reducing the deviation between the two by 34.6 percent for a standalone LSTM and by 56.5 percent when the LSTM is coupled to Gaussian process regression, the most reliable of the configurations tested. Because the method models the total forecast error directly, it captures the full predictive uncertainty that matters for decisions, rather than only the model contribution that dropout-based approaches estimate.