非常规气藏储层改造体积评价方法研究

为精确描述非常规气藏压裂后的复杂流动特征及定量评价储层改造体积（SRV），利用自主研发的缝网重构算法“破裂树生长法”建立压后缝网模型，并以此为基础提出了使用拟稳态流动时特定的压力等值线来确定SRV范围的定量评价方法，最后以长宁201井区为例进行了矿场实例分析。该方法根据微地震监测点的位置，重构出微裂缝网的连通关系。在复杂微裂缝网的基础上建立离散裂缝地质模型并进行生产数值模拟，根据数值模拟结果的压力分布精确划定SRV的范围。利用该方法计算出长宁201井区的SRV体积为0.052 1 km3，以该缝网模型的数值模拟产量预测结果符合实际生产规律，方法实用性较好。     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study

This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is preferable to the row normalization suggested by Diebold and Yilmaz since it yields net spillovers free of sign and ranking errors.     

Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.     

Nonlinear Factor-Augmented Predictive Regression Models with Functional Coefficients

This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology.     

复频电导技术在隧洞超前探水中的应用

围岩富水带是隧道超前预报最关注的问题，目前，多使用电磁方法来预报围岩的含水性，但受到场地条件限制，面临掌子面条件、金属机具干扰、三维波场定位等诸多困难，特别是掌子面前方100 m范围内围岩的含水性预报，在国内外都是一个新的高难课题。在研究了岩体电导率与电容率复频特性的基础上，结合巴基斯坦NJ-TBM引水隧洞工程，开发了复频电导探水(CFC)技术。该技术基于电磁波反射与相干原理，选用100 kHz～10 MHz频率范围，采用电偶极子发射与阵列接收方式和偏移成像技术，突破了隧道内场地条件的限制。依据1/4相干波长确定含水体的位置，相干能量确定含水量的大小。在巴基斯坦NJ-TBM隧洞超前探水的应用，证明了该方法的可行性与有效性。      

Reinforcement learning for electricity dispatch in grids with high intermittent generation and energy storage systems: A case study for the Brazilian grid

Intermittent energy sources such as wind and solar have recently been growing a lot faster than dispatchable energy sources in Brazil, which made investments in energy storage systems become an attractive possibility in the country. Current operational policies for energy dispatch do not consider storage systems and need adjustments to fit this technology. With this motivation, we use reinforcement learning techniques to develop policies for managing storage systems in a grid that can handle time-varying inputs and loads, with rolling forecasts. We use a deterministic lookahead (DLA) policy which has been parametrically modified to perform well in the presence of uncertain forecasts. For realistic simulations, the base model considers important characteristics in a grid that influence the interaction between scheduling and real-time operation such as power and ramping capacities, notification times, and stochastic forecasts. The parametric modification with tunable parameters allows an optimal balance between two conflicting services provided by the storage system: time-shifting and spinning reserves. Optimal reserves ranged from 35% to 100%, depending on the tested dataset, which shows the importance of tuning. Differently from stochastic lookahead policies, which are computationally expensive, parameterized DLA policies can be applied to real-time operation after being optimized in a stochastic base model.     

基于ECMWF和WRF的嘉陵江流域面雨量预报精度评估

将降雨数值预报产品运用到水文预报中已经逐渐成为提高洪水作业预报的预见期的重要手段。为充分了解ECMWF(European Centre for Medium Range Weather Forecasts)和WRF(Weather Research and Forecasting model)2种数值天气预报产品对嘉陵江研究区面雨量预报的预报精度和误差分布,且为增强洪水预报精度的稳健性提供科学支持,采用TS评分、空报率、漏报率、正确率等指标,对嘉陵江地区7个气象分区内的2016年汛期面雨量预报结果进行了检验,分析了不同分区内各检验指标与预报时效的关系。结果表明:ECMWF数值预报产品和WRF数值预报产品均可用于该地区晴雨预报,且2种产品的预报精度随降水等级的增大呈增大趋势,随预报时效的增加呈减小趋势。综合而言,ECMWF数值预报产品对嘉陵江研究区的预报效果更好。     

低碳环境下钢铁企业燃气系统动态平衡调度仿真

在“节能减排、降本增效”的大背景下,从碳素流视角切入,在煤气介质预测基础上,建立了0-1混合线性规划模型,以能源成本和碳交易成本总和最小化为目标,研究钢铁行业副产煤气生产调度问题。采用基于遗传算法的动态迭代调度算法,通过混合交叉算子和平均变异算子提高遗传算法局部搜索能力,并以国内某大型钢铁企业为仿真对象进行系统仿真,结果表明:混合煤气柜可以根据煤气产耗特点调整存储量,并保证存储量在合理范围内;调节用户会起到一定的缓冲作用,可以根据某种副产煤气的产耗特点响应增加或减少该种煤气的消耗量;当某种副产煤气严重不足时,固定消耗用户会根据优先级响应停调,以满足生产需求。可见副产煤气的优化调度应用可以为中国钢铁行业的低碳发展和节能减排提供技术支撑。     

基于模糊模式识别的煤与瓦斯突出区域预测

借助于成熟的模糊理论与技术可以较客观地实现突出预测中不精确信息与不精确关系的正确表达与处理，综合考虑突出影响因素，提出了煤与瓦斯突出区域预测的模糊模式识别新方法.首先采用模糊聚类分析对煤与瓦斯突出的样本集合进行分类，建立不同程度的模糊模式.然后对待预测样本进行模糊模式识别，根据择近原则，哪一种模式与预测样本最接近，待预测样本即属于哪一种突出类型.此方法克服了模糊聚类单一分析方法的不确定性，实现了多指标定量化的预测，提高了预测效果的准确程度.通过实例验证，证明了预测的可靠性.