Monthly Joint Operations for the Nakdong Multi-reservoir System in Korea

Abstract

This study applies a state-of-art optimization technique, SSDP/ESP (Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction), to derive a monthly joint operating policy for the Nakdong multi-reservoir system in Korea. A rainfall-runoff model, SSARR (Streamflow Synthesis And Reservoir Regulation), is linked to the SSDP/ESP model to provide ESP scenarios for runoff during the next month in the Nakdong River basin. The primary advantage of the SSDP/ESP is that it updates the derived operating policy as new ESP forecasts become available. Another SSDP model that employs historical runoff scenarios (SSDP/Hist) is also developed. The main difference between the two SSDP models is that SSDP/Hist is an off-line model whereas the SSDP/ESP is on-line. The developed operating policies are tested with a simulation model using an object-oriented simulation software, STELLA. The simulation results show that SSDP/ESP is superior to SSDP/Hist with respect to the water supply criterion, although both models perform similarly with respect to the hydroelectric energy production criterion.     

基于近邻估计的年径流预测动态 联系数回归模型

集对分析理论为处理确定、不确定系统提供了新的途径,根据集对分析理论建立起来的预测联系数回归模型可以明显改善回归模型的预测精度。对于预测因子结构具有的动态性,文中将利用近邻估计,通过计算各个预测因子的变异系数,来判断预测因子在某次预测中处于强势或者弱势,进而动态地选择预报功能大的强势因子,消除对预报起负面作用的弱势因子的作用,这样很好地体现了预测因子结构中具有的动态性。基于此建立了基于近邻估计的年径流预测动态联系数回归模型（NNE-DCNR）。结果说明：用NNE-DCNR去预测年径流量,预测精度比常用预测方法有显著提高,在水文水资源的预测中具有推广应用价值     

Analysis and numerical study of a hybrid BGM-3DVAR data assimilation scheme using satellite radiance data for heavy rain forecasts

A fine heavy rain forecast plays an important role in the accurate flood forecast, the urban rainstorm waterlogging and the secondary hydrological disaster preventions. To improve the heavy rain forecast skills, a hybrid Breeding Growing Mode (BGM)-three-dimensional variational (3DVAR) Data Assimilation (DA) scheme is designed on running the Advanced Research WRF (ARW WRF) model using the Advanced Microwave Sounder Unit A (AMSU-A) satellite radiance data. Results show that: the BGM ensemble prediction method can provide an effective background field and a flow dependent background error covariance for the BGM-3DVAR scheme. The BGM-3DVAR scheme adds some effective mesoscale information with similar scales as the heavy rain clusters to the initial field in the heavy rain area, which improves the heavy rain forecast significantly, while the 3DVAR scheme adds information with relatively larger scales than the heavy rain clusters to the initial field outside of the heavy rain area, which does not help the heavy rain forecast improvement. Sensitive experiments demonstrate that the flow dependent background error covariance and the ensemble mean background field are both the key factors for adding effective mesoscale information to the heavy rain area, and they are both essential for improving the heavy rain forecasts.     

A physical approach of the short-term wind power prediction based on CFD pre-calculated flow fields

A physical approach of the wind power prediction based on the CFD pre-calculated flow fields is proposed in this paper. The flow fields are obtained based on a steady CFD model with the discrete inflow wind conditions as the boundary conditions, and a database is established containing the important parameters including the inflow wind conditions, the flow fields and the corresponding wind power for each wind turbine. The power is predicted via the database by taking the Numerical Weather Prediction (NWP) wind as the input data. In order to evaluate the approach, the short-term wind power prediction for an actual wind farm is conducted as an example during the period of the year 2010. Compared with the measured power, the predicted results enjoy a high accuracy with the annual Root Mean Square Error (RMSE) of 15.2% and the annual MAE of 10.80%. A good performance is shown in predicting the wind power’s changing trend. This approach is independent of the historical data and can be widely used for all kinds of wind farms including the newly-built wind farms. At the same time, it does not take much computation time while it captures the local air flows more precisely by the CFD model. So it is especially practical for engineering projects.     

基于可公度理论的关联规则算法在干旱中的预测研究

利用可公度理论中的三元-五元可公度式及其拓展式,从粗粒度角度对郑州地区从1901年-2000年严重干旱等级以上记载年份进行可公度数统计和分析,依据预测结果结合数据挖掘理论中的关联规则算法作进一步推演和修正,结果表明该方法预测准确率达80%,同时预测从2013年-2015年郑州市不会发生严重以上等级干旱。     

基于小波分析的滑坡变形规律研究

对于有多个GPS监测点的滑坡,通常只研究个别代表性较强的点的位移,以此近似反映滑坡整体变形情况,但这种以点代面的方法与实际相差较大。现利用三峡库区某滑坡长期地表位移监测资料,根据各监测点位移对滑坡整体位移的贡献率,求得综合位移曲线,再利用小波分析等数学方法推导综合位移方程,据此分析了外界因素(降雨、库水位)对滑坡变形影响机理及周期。结果表明:该滑坡现处于潜在不稳定状态,降雨和库水位对滑坡的影响周期约为12个月。应用滑坡总位移方程进行验证性预测后显示,其预测精度达到92%,因此该方程能更真实地反映滑坡稳定性现状和发展趋势,对同类滑坡的研究具有借鉴价值。     

PSO-BP神经网络在MBR工艺中的膜污染预测

针对MBR膜污染因子较为复杂且各因子之间相互交叉,提出基于PSO-BP神经网络的膜污染预测方法.首先用主成分分析法实现输入变量的去维和去相关,简化网络的输入,然后应用粒子群算法优化神经网络的权值和阈值.网络训练时使用的数据是在不同操作条件下,采用孔径为0.038μm的聚醚砜超滤膜处理印染废水溶液时得到的膜通量实验数据,最后用训练好的PSO-BP神经网络对膜通量进行预测.结果表明,与传统BP算法相比,PSO-BP神经网络算法能更快实现收敛,提高运算速度以及膜通量预测的准确度.     

基于回声状态网络的飞机混沌时间序列预测模型

准确检测飞机即将发生的故障或预测其状态的变化趋势,对于实现飞行安全具有重要意义。文章针对传统基于回声状态网络在故障预测中的不足,构建了基于小波降噪的回声状态网络预测模型。该模型保留了非线性时间序列回声状态网络预测的优势,并采取小波变换对混沌时间序列进行降噪预处理,有效提高了含噪混沌时间序列的预测精度。论文通过对某飞机发动机滑油散热器温度时间序列数据序列进行预测分析,表明文中模型具有较好的预测精度,验证了模型的有效性。     

特殊岩性油气藏地层压力预测技术研究进展

阐述传统地层压力预测方法在特殊岩性储层应用时的缺陷,分析碳酸盐岩、火成岩和变质岩地层异常高压的成因机制,归纳现有该类储层中地层压力预测方法,并提出该类储层地层压力预测方法的发展趋势.     

基于ARMA-BP神经网络模型的风电功率预测研究

结合ARMA时间序列预测模型和BP神经网络预测模型的优点,构建ARMA-BP神经网络混合模型,对风电功率预测问题进行研究。采用构造性神经网络模型得出的类别值对统计时间序列模型的预测值进行修正,提高了风电预测的精度和效率。