Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting
Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting
复制标题
短期太阳能概率预测天气预报的分位数回归后处理
DOI：
10.3390/en11071763
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发表时间：
2018
期刊：
影响因子：
3.2
通讯作者：
M. Marrocu
中科院分区：
文献类型：
--
作者：
L. Massidda;M. Marrocu
光伏发电在配电网中的列入提出了与太阳能源的可变性有关的技术困难，并确定了概率预测程序（PF）的需要。这项工作描述了一种新的方法，PF分位数回归的基础上，使用由欧洲中期天气预报中心（ECMWF）综合预报系统（IFS）和Enhancement预测系统（EPS）的数值天气预报的连续性增强回归树（GBRT）的方法。所提出的方法进行比较，预测与分位数回归仅使用IFS预测（QR），与未校准的EPS预测和EPS预测校准的方差赤字（VD）的程序。所提出的方法产生的预测与时间分辨率等于或优于气象预报（1小时的IFS和3小时的EPS），并在审查的情况下，能够提供更高的性能比其他方法获得的预测范围高达72小时。
The inclusion of photo-voltaic generation in the distribution grid poses technical difficulties related to the variability of the solar source and determines the need for Probabilistic Forecasting procedures (PF). This work describes a new approach for PF based on quantile regression using the Gradient-Boosted Regression Trees (GBRT) method fed by numerical weather forecasts of the European Centre for Medium Range Weather Forecast (ECMWF) Integrated Forecasting System (IFS) and Ensemble Prediction System (EPS). The proposed methodology is compared with the forecasts obtained with Quantile Regression using only IFS forecasts (QR), with the uncalibrated EPS forecasts and with the EPS forecasts calibrated with a Variance Deficit (VD) procedure. The proposed methodology produces forecasts with a temporal resolution equal to or better than the meteorological forecast (1 h for the IFS and 3 h for EPS) and, in the case examined, is able to provide higher performances than those obtained with the other methods over a forecast horizon of up to 72 h.