A Search for Hidden Relationships: Data Mining with Genetic Algorithms

This paper presents an algorithm that permits the search for dependencies among sets of data (univariate or multivariate time-series, or cross-sectional observations). The procedure is modeled after genetic theories and Darwinian concepts, such as natural selection and survival of the fittest. It permits the discovery of equations of the data-generating process in symbolic form. The genetic algorithm that is described here uses parts of equations as building blocks to breed ever better formulas. Apart from furnishing a deeper understanding of the dynamics of a process, the method also permits global predictions and forecasts. The algorithm is successfully tested with artificial and with economic time-series and also with cross-sectional data on the performance and salaries of NBA players during the 94–95 season.     

Strategic appraisal of energy models for Mozambique

The needs that an energy supply system must meet are constantly changing, due to technological, social and political reasons. Effective energy planning is a dynamic process that is repeated periodically and adjusts to changing conditions. Energy decision makers and planners are no longer able to rely on inductive decision making since they have to investigate the effect of various decision parameters and possible future changes. To help in this process, models have been developed where estimates of future load growth, candidate power plants, fuels and other key factors can be introduced, from which the planners can evaluate decision parameters and the available alternatives. The paper presents the different methodologies and practices that are used by 11 energy models for energy demand forecasting, supply side management and generation expansion planning, demand side management and integrated resource planning. The paper concludes to the presentation of a strategic appraisal of the examined energy models appropriate for energy planning in Mozambique. Three models are proposed for conducting demand forecasting, generation expansion planning and demand side management. Copyright © 2002 John Wiley & Sons, Ltd.     

非方程灰色系统方法在长期水文预报中的应用初探

基于水文过程的复杂性和影响因子信息收集不完善这一基本事实，本文将流域水文系统视为含有灰元和灰信息的灰色系统，并初步实践了非方程灰色预报方法在长期水文预报中的应用。方法在一定意义上脱离了传统的以方程为中心的预报模式，它既考虑预报因子对预报量的不同影响程度，又不过分强调预报量与预报因子的具体相关函数形式，为长期水文预报提供了一种新思路。文中列出了实例，效果令人满意。     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.     

模拟退火算法用于蒸汽吞吐井生产能力的预测

模拟退火算法是一种利用统计力学观点求解优化问题全局最优解的方法,是目前解决优化问题的一种比较先进的方法。本文应用模拟退火算法对蒸汽吞吐注采参数进行优化设计,并确定出了6个吞吐周期的最优周期产油量和周期注蒸汽量,较实际周期产油量和实际周期注汽量均有明显增加。将各周期的优化累积产油量和优化累积注汽量画在累积产油量与累积注汽量的双对数坐标中,得出累积注汽量和累积采油量关系曲线(简称注采关系曲线)。将该注采关系曲线外推估算了该井未来周期的累积产油量和累积注汽量,从而达到对蒸汽吞吐井生产能力的预测。     

BP网络过拟合问题研究及应用

通过类比信息传递过程中的一般测不准关系式,研究BP网络出现过拟合现象时,代表网络学习能力的权值改变量与反映网络推广能力的未参与训练的检验样本的辨识误差之间的关系.在该关系式中引入一个过拟合参数P,通过对岷江流域紫坪埔站的流量预报,说明在判断网络是否出现过拟合现象时,计算过拟合参数P是一种有研究意义的参考.     

变权组合预测模型在大坝安全监测中的应用

简要介绍了组合预测模型的基本原理，该模型改进了单一模型考虑因素的片面性，但常规的组合预测模型的权系数是固定的。考虑到各种模型的预测精度的变化，以及大坝安全监测数据存在的突变、时效等因素，固定权系数的组合预测模型有待进一步改进。文中提出了变权系数组合预测模型，并将其用于实际的大坝安全监测工程。结果表明，变权组合预测模型的预报精度高于其他模型。是一种值得推广的方法。     

基于决策树技术的日前市场清算电价预测

以澳大利亚昆士兰州电力市场为背景,采用决策树技术,建立了提前日的电力市场清算电价预测模型。详细介绍了决策树技术的原理及其在电价预测中的实现方法,考虑了非电价因素对电价预测的影响。实际结果表明该方法具有良好的预测效果和直观性。     

中国新闻纸需求量灰色预测

针对我国新闻纸需求量大且具有不确定性的特点,运用具有适用性广,预测准确率高等优点的灰色系统理论预测了我国新闻纸需求量,建立了需求量模型并对该模型的精度进行了检验。预测结果表明,该模型用于预测我国新闻纸需求量具有一定的可行性和有效性。     

地质超前预报和动态施工在隧洞施工中的应用

通过在直峰架洞施工过程中，地质超首预报与隧洞动态施工的结合，将国际岩体质量分级到生产实践中，针对不同级别的岩级，应用相应的施工方案，以达到为隧洞篱工总结经验的目的。