THE EXPERIMENTS OF SEASONAL PREDICTION USING THE ANALOGY-DYNAMICAL MODEL

From the viewpoint of dynamics, it is convenient to regard the field to be predicted as a small disturbance superposed on the historical analogous field, and thus the statistical technique can be used in combining with the dynamics. Along this line, a coupled atmosphere-earth surface analogy-dynamical model is formulated and applied to making eight seasonal predictions. All of the predictions were initiated from January and have been made from February to August of 1981 to 1988. The experiments of eight-year predictions show certain skill in seasonal prediction, and the skill scores of prediction are greater than those of single statistical analogy forecast.     

宁波城区多层砖房搞抗震鉴定与加固

经过对宁波城区现有的多层砖房详细的调查与研究，进行了抗震能力的鉴定和地震震害的预测，并提出了抗震加固的措施。     

Price forecast in the competitive electricity market by support vector machine

The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter C_i and ε_i assigned to each training data, the flexible C_i Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. For Day Ahead Market (DAM) price forecast, the load, time of use index and index of day type are taken as the major factors to characterize the market price, therefore, they are selected as the inputs for the flexible SVR forecast model. For the long-term price forecast, we take the reserve margin R^m, HHI and the fuel price index as the inputs, since they are the major factors that drive the market price variation in long run. For short-term price forecast, besides the detailed analysis with the young Italian electricity market, the new model is tested on the experimental stage of the Spanish market, the New York market and the New England market. The long-term forecast with the SVR model presented is justified by the forecast with the data from the Long Run Market Simulator (LREMS).     

A combination selection algorithm on forecasting

It is widely accepted in forecasting that a combination model can improve forecasting accuracy. One important challenge is how to select the optimal subset of individual models from all available models without having to try all possible combinations of these models. This paper proposes an optimal subset selection algorithm from all individual models using information theory. The experimental results in tourism demand forecasting demonstrate that the combination of the individual models from the selected optimal subset significantly outperforms the combination of all available individual models. The proposed optimal subset selection algorithm provides a theoretical approach rather than experimental assessments which dominate literature.     

基于BP神经网络的图像质量评价参数优化

 在基于噪声图像的无参考峰值信噪比质量评价方法中,为了得到最优的阈值参数,提出以图像块均方误差阈值threshold1、噪声检测阈值threshold2为输入因子, 以Pearson相关系数和Spearman等级相关系数为输出因子, 以实验值为样本建立［2 7 2］单隐层BP神经网络模型,应用BP神经网络的泛化能力实现对相关阈值参数的预测优化,为阈值参数的选择提供理论依据。实验结果表明,所建立的数学模型可靠,预测结果与试验值的偏差小,训练好的BP神经网络能够比较准确地预测不同阈值参数下的相关系数。优化后,选取threshold1=101,threshold2=4,Pearson相关系数达到了-0.895 0,Spearman等级相关系数达到了-0.913 6,评价效果得到提高,且节省大量时间。     

基于组合预测的静态利率期限结构优化模型

针对单个静态利率期限结构模型在拟合收益率曲线时的不足,本文引入组合预测的方法,在绝对误差和与方差和最小准则下,分别建立了静态利率期限结构组合优化模型,并给出了模型的遗传算法求解过程。然后将上海证券交易所2004～2009年的国债每日交易数据分为样本内数据和样本外数据,对多项式样条、指数样条、NS、SV和组合优化模型进行实证比较。结果表明：无论是对于样本内数据的拟合,还是对于样本外数据的预测,组合优化模型的统计特征指标几乎都要优于其他单一模型,并且具有良好的适应性和稳健性,适用于拟合我国国债利率期限结构。     

百年化学的一些回顾、反思和展望——2011国际化学年感言

为了纪念2011年国际化学年,在本文中,我们对百年来化学所取得的巨大成就和某些不足分别进行了一些回顾和反思,也展望了未来化学发展的美好前景,并强调地指出化学对人类的可持续发展必将做出更大的贡献.     

基于SCE-UA算法的多模式降雨集成预报技术研究

高精度的降雨预报对于防汛决策具有重要指导意义. 以中国气象局GRAPES_MESO模式、中国气象局T639模式、美国国家环境预报中心GFS模式和上海区域中尺度SMS-WARMS模式为基础, 采用SCE-UA算法估计各预报模式权重系数, 通过加权平均建立多模式降雨集成预报, 并以宁波市2016年8月1日至2016年10月31日和2017年9月1日至2017年11月30日的降雨过程为例, 从均方根误差、相关系数、TS评分等多个方面分析集成预报的预报效果. 结果表明: 基于SCE-UA算法的多模式降雨集成预报的预报效果不仅优于各单一预报模式, 而且优于遗传算法和BP神经网络集成预报, 表现出较好的适用性.     

美国光伏市场调研及前景研究

为加强对美国光伏市场情况的研究,对美国太阳能资源、光伏装机、光伏系统价格、光伏相关政策等资料进行了收集整理,并对美国光伏行业的发展前景进行了分析。参考美国能源部、太阳能学会等提供的光伏行业相关资料,对美国光伏行业概况进行概括梳理;通过查找光伏政策相关资料并与美国投资银行、光伏项目开发商进行座谈,对影响美国光伏行业的投资信用补贴、可再生能源配额标准等政策进行介绍;最后,结合美国太阳能学会研究成果,对美国光伏行业的发展前景提出预期。在美国光伏补贴政策的影响下,美国光伏装机截止2016年已超过40 000 kW,世界排名第四位,预计2018年光伏装机增长将放缓,2019-2022年光伏装机增长将逐步加快。     

滑坡时间预报的非平稳时间序列方法研究

本文基于边坡变形破坏的特点,对位移观测数据进行统计分析,利用非平稳时间序列理论就位移观测值建立模型进而作出预报。通过实例验证,该理论方法在边坡变形破坏时间预报中是有效可行的。