Accuracy of wind energy forecasts in Great Britain and prospects for improvement

The metric representing the wind energy forecast error, when reported as a percent, is calculated quite differently than the error metrics for electricity transmission, electricity load, or in other industries such as manufacturing when they are also reported as a percent. The resulting calculated metric is quite different from what would be reported if the method utilized elsewhere was employed. This paper examines the possible forecast assessment and operational challenges associated with this finding. Concerning the prospects for improvement, the errors reported in MW of energy have a systematic component. With this insight, we developed a model to improve accuracy.     

2010-2020年深圳市机动车发展和油气需求量预测

介绍了深圳市机动车发展现状,分别用趋势外推法、城市比较分析法和分类法预测了2010-2020年深圳市的机动车保有量,最后预测了机动车的油气需求量。     

洪水水文预报系统

水文预报系统是计算机算法应用于水文预报的一次实践 ,在从产汇流原理出发 ,详细阐述系统结构 ,工作原理及系统特征 ,还对软件的应用及今后开发前景进行了展望 ,随着社会发展和科技进步 ,实施计算机预报系统便于提高预报精度 ,增强水文预报的时效性 ,将使水文预报作用日益显著。     

嘉陵江上游区滑坡泥石流预警信息系统初步研究

本文在嘉陵江上游区滑坡泥石流预警网络的基础上,结合我国及嘉陵江上游区的具体情况和经济承受能力,提出了嘉陵江上游区滑坡泥石流预警信息系统的基本结构、内容、运行原理。此系统的建立对于当地的减灾、防灾和经济发展具有重要意义。     

Burned area forecasting using past burned area records and Southern Oscillation Index for tropical Africa (1981-1999)

An 18-year time series of monthly NOAA-AVHRR Pathfinder Land burned area was analyzed for the region of tropical Africa, from July 1981 to June 1999. The transition period between NOAA-11 and NOAA-14 platforms from July 1993 to June 1995 was not included due to missing and outlier data. Stability of the time series was addressed for the input variables in the burned area algorithm, reflectance and temperature channels.A Seasonal AutoRegressive Integrated Moving-Average (SARIMA) model was developed for forecasting potential burned area. The SARIMA model identified an autoregressive regular term with 1-month lag and an autoregressive 12-month seasonal term with one season (12 months) component. A cross-correlation between Southern Oscillation Index (SOI) and burned area was statistically significant predictor variable in a time series with 20-month lag. Results show that the SARIMA model with this predictor improved both, fitting and forecasting, residual variance, by 4.1% and 5.6%, respectively, thereby, demonstrating potential relationship between SOI and burned area for the study region. Forecasting was estimated by considering only the first 16 years of the monthly burned area in the time series, from July 1981 to June 1997. The prediction for the following 24 months (from July 1997 to June 1999) was within the 95% confidence level indicating that the forecast was a valid characterization of the modeled process.     

基于交互式逐步回归的双语教学态度预测模型

针对多元线性回归分析法预测双语教学态度误差较大的问题,通过引入变量的平方项及交叉乘积项建立拟线性回归方程,利用交互式逐步回归分析法对变量进行筛选,并以筛选后的变量建模。将调查数据分为建模样本数据和测试样本数据,测试结果表明经过变量扩维及筛选所建立的预测模型精确度有较大提高,为语言学研究提供了一种新方法,值得推扩和借鉴。     

Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.     

基于AR IMA与LOESS的安全生产事故时序预测研究

我国每年安全生产事故都造成大量的人员伤亡和经济损失,因此预防和减少安全生产事故的发生非常重要,利用ARIMA模型和LOESS回归模型组合预测能提高安全生产事故次数预测的精准度.首先建立ARIMA预测模型,用训练集中的预测偏差建立LOESS回归模型,综合两者的预测值,得到最终预测结果.采用2007年9月至2016年7月全国安全生产事故次数数据的实验结果表明:综合2种模型得到的组合预测方法的预测结果精度高于单种模型.     

张力减径过程快速仿真系统建模问题的研究

在空间解析曲面模型理论基础上,采用三维弹塑性大变形有限元方法,建立了复杂几何形体动态约束模型,解决了张力减径过程中的矩阵变换、模型转换和多机架全系统仿真的问题。     

中国铸造市场的气候与机遇

我国近几年铸件产量统计；国内工业用户需求和使用铸件数量；中国进出口状况，综述了我国铸造行业所采用的主要生产工艺，调查了部分具有典型代表性的工厂其生产成本构成，依据现有资料分析，旨出我国是铸件生产的大国，却不是铸件生产强国，提出了今后5年中国铸造市场展望，以及对铸件产量估计，市场展望和投资前景，抓住机遇，大展宏图。