耦合偏互信息的贫资料地区小水电发电能力预测

Power Generation Capacity Forecast of Small Hydropower Combined with Partial Mutual Information in Data Poor Area

准确预测小水电富集地区小水电的发电能力是保证电网安稳运行、实现大小水电协调的重要措施。不同于大中型水电,小水电大多位于偏远山区,信息采集困难,管理薄弱,可用于发电能力预测的资料较少,难以利用和借鉴现有的大中型水电发电能力预测方法。文中结合小水电的实际情况,以地区小水电整体为对象,提出了耦合偏互信息的小水电发电能力预测方法。该方法以BP神经网络预测模型为手段,采用偏互信息方法筛选显著影响小水电发电能力的预报因子,并结合气象预测系统(CFS)的气象预报信息作为输入,实现贫资料地区小水电发电能力预测。最后,以云南小水电富集的德宏和大理为实例研究验证了所述方法的有效性。

In a small hydropower (SHP) enriched area,accurate forecast of SHP generation is very important for ensuring power grid safety and coordinating SHP generation with other power sources.Different from the generation forecast of large hydropower,most SHP stations are located in remote mountainous areas where information gathering is hard and management weak,making it nearly impossible to make use of the locally available forecast methods.By considering the actual situation of SHP stations,and with the SHP stations of an area as the object,a SHP generation forecast method is proposed.This method uses the improved BP neural network as the forecast model and partial mutual information to select remarkable input variables. In the power forecast stage,climate forecast system (CFS) data is used to get the precipitation of the target area and as the input of the model to forecast SHP generation.The case study of Dehong and Dali in Yunnan shows the effectiveness of the proposed method.