基于LS-SVM的混凝土断裂参数回归与预测

选择最小二乘支持向量机对混凝土测量中存在的大样本数据进行回归分析以及预测。研究表明:利用LS-SVM模型进行混凝土测量当中存在的大样本数据回归分析具有精度高,速度快等优点。模型不需建立方程式,修改容易,可处理被干扰的数据,具有较强的概括性。利用LS-SVM对冻融作用下的混凝土的断裂韧度进行回归分析以及断裂预测,模型较好的反映了断裂韧度的下降趋势。     

基于滚动的BP神经网络的光伏发电功率预报

鉴于准确预测光伏发电功率可减少大规模光伏并网发电对电网造成的冲击,以河南省某并网的光伏发电站7~11月共5个月的数据为例,通过建立非滚动的和滚动的BP神经网络模型,分别进行数值预报辐射订正和发电功率预报,并对预报的72h结果分为第1、2、3d分别进行检验。结果表明,滚动的BP神经网络对辐射订正和功率预报均具有较好的泛化能力,方法简便、实用,能够有效降低光伏发电功率的预报误差。     

炼油加热炉空气预热器的发展与展望

空气预热器是加热炉的重要设备,在炼油厂主要起到节能减排的作用,随着国家能源政策和环保政策的日益加强,其重要性愈发突出。通过对现有炼油加热炉空气预热器的种类、性能进行分类、对比,指出其优缺点及发展方向,并结合实际对将来的空气预热器进行展望。     

Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning

Numerical weather forecasts, such as meteorological forecasts of precipitation, are inherently uncertain. These uncertainties depend on model physics as well as initial and boundary conditions. Since precipitation forecasts form the input into hydrological models, the uncertainties of the precipitation forecasts result in uncertainties of flood forecasts. In order to consider these uncertainties, ensemble prediction systems are applied. These systems consist of several members simulated by different models or using a single model under varying initial and boundary conditions. However, a too wide uncertainty range obtained as a result of taking into account members with poor prediction skills may lead to underestimation or exaggeration of the risk of hazardous events. Therefore, the uncertainty range of model-based flood forecasts derived from the meteorological ensembles has to be restricted.In this paper, a methodology towards improving flood forecasts by weighting ensemble members according to their skills is presented. The skill of each ensemble member is evaluated by comparing the results of forecasts corresponding to this member with observed values in the past. Since numerous forecasts are required in order to reliably evaluate the skill, the evaluation procedure is time-consuming and tedious. Moreover, the evaluation is highly subjective, because an expert who performs it makes his decision based on his implicit knowledge.Therefore, approaches for the automated evaluation of such forecasts are required. Here, we present a semi-automated approach for the assessment of precipitation forecast ensemble members. The approach is based on supervised machine learning and was tested on ensemble precipitation forecasts for the area of the Mulde river basin in Germany. Based on the evaluation results of the specific ensemble members, weights corresponding to their forecast skill were calculated. These weights were then successfully used to reduce the uncertainties within rainfall-runoff simulations and flood risk predictions.     

A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

Reliable real-time probabilistic flood forecasting is critical for effective water management and flood protection all over the world. In this study, we develop a real-time probabilistic channel flood-forecasting model by combining a channel hydraulic model with the Bayesian particle filter approach. The new model is tested in the upstream river reach of Three Gorges Dam (TGD) on the Yangtze River, China. Stage observations at seven hydrological stations are used simultaneously to adjust the Manning's roughness coefficients and to update discharges and stages along the river reach to attain reliable probabilistic flood forecasting. The synthetic experiments are applied to demonstrate the new model's correction and forecasting performances. The real-world experiments show that the new model can make accurate flood forecasting as well as derive reliable intervals for different confidence levels. The new probabilistic flood forecasting model not only outperforms the existing deterministic channel flood-forecasting models in accuracy, but also provides a more robust tool with which to incorporate uncertainty into flood-control efforts.     

TGP206在庙子梁隧道斜井地质超前预报中的应用

介绍了TGP206超前地质预报系统的原理和使用方法,同时,结合庙子梁隧道斜井施工实例,阐述了TGP206在隧道地质超前预报中的实用性和有效性,根据现场反馈的地质资料,证明了TGP206超前隧道预报仪具有较好的准确性,能够有效的指导工程施工。     

Energy needs for Morocco 2030, as obtained from GDP-energy and GDP-energy intensity correlations

We present forecasts of the energy consumption of Morocco towards 2030. Two models have been developed and their results compared: one based on the energy intensity (IE) and another one on a link with the country urbanization rate (URB). The IE model allowed to segment energy consumption in four posts while the URB model only in two posts. For the sensitivity analysis to economic growth, three future GDP evolution scenarios are proposed. The retrospective correlations of both models are excellent but their future extrapolations finish in slightly different results. Through their correlation to electricity consumption, peak power forecasts are also presented. A forecast of the country energy intensity is commented. As the average yearly increase of electricity should still be between 4.9% and 7.1% during 2020–2030, the electric equipment program continuation after 2020 must soon be clarified and avoid the former implementation delays. As the white combustibles needs should yearly increase between 6.3% and 7.8% in 2020–2030, electrical equipment programs should also make provisions for the case of deployment of electric cars. Butane subsidies widen the gap with other fuels and must be removed very soon possible to reduce the growth of its consumption and energy intensity.     

基于ARIMA的电网地磁感应电流预测研究

地磁感应电流(GIC)对电网安全运行会带来影响,GIC的分析与预测是近来研究的重点,文中应用ARIMA模型对某一时段内的GIC进行建模分析,挖掘其内在的趋势规律,并运用建立的模型进行短期预测.所建立的模型通过了适应性检验和参数的显著性检验,误差在(8％～21％)范围内,能较好的应用于GIC的预测,为保证电网的安全运行提供理论支持.     

包钢结构调整与产品的开发方向

文章对未来5年世界钢材进出口贸易流量进行了评估,根据需求发展内在和外部因素的分析,分品种进行了需求预测。结合包钢的生产现状,详细分析了产业和产品结构状况,为包钢在"十二五"期间及未来结构调整中,重点开发的钢材品种指出了方向。