Matching of technological forecasting technique to a technology using fuzzy multi-attribute decision-making methods: Case study from the aerospace industry

Given the accelerating pace of technological advances and environmental changes, technology-based companies are required to predict and understand future events in their environments. However, there is a wide range of forecasting methods creating confusion on which method to use. This paper demonstrates the selection of an appropriate technique for technology forecasting in the Iran Aviation Industries Organization (IAIO). To this end, a review of the literature was first reviewed to extract the proper criteria for selecting a forecasting method. Next, the SWARA and fuzzy MUTLIMOORA methods were used to evaluate and prioritize a total of twelve forecasting methods proposed for the case study. The results suggested that the Delphi method for technology forecasting in the IAIO. Scenario writing and the relevance tree are the next proper alternatives that can be used.     

基于IFOA-GRNN的电力系统短期负荷预测

考虑到电网负荷与诸多因素有关，设计了一种带有温度、气象、日期类型的广义回归神经网络（GRNN）负荷预测模型。为了提高该模型的预测精度，提出了一种改进果蝇优化算法优化广义回归神经网络（IFOA-GRNN）的方法，即在利用果蝇优化算法（FOA）进入迭代寻优时，通过改进搜索距离优化该算法的性能和稳定性。利用改进的FOA优化GRNN的光滑参数，然后利用训练好的预测模型对甘肃省某地区进行了短期负荷预测，并与FOA-GRNN和误差反向传播神经网络(BPNN)模型结果进行了误差比较。结果表明， IFOA-GRNN具有较高的预测精度，能够满足电力系统短期负荷预测的要求。     

基于SARIMA-GRNN-SVM的短期商业电力负荷组合预测方法

针对短期商业电力负荷预测准确性与周期难以满足现有电力现货市场的问题,提出了一种基于SARIMAGRNN-SVM(seasonal autoregressive integrated moving average-generalized regression neural network-support vector machine)的商业电力负荷组合预测模型。首先,对商业电力负荷变化的周期规律与随机因素的复杂影响进行了分析;然后,结合以上分析,选用SARIMA和GRNN为单一预测模型对商业电力负荷进行预测,并利用SVM进行组合,实现日前商业电力负荷预测;最后,通过某商业综合体的电力负荷数据进行验证。所提组合预测模型较单一预测模型拥有更优的预测精度与鲁棒性,可以为短期商业电力负荷预测提供借鉴。     

State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

Evolving accuracy: A genetic algorithm to improve election night forecasts

In this paper, we apply genetic algorithms to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market research, especially due to the fact that decisions have to be made within seconds on live television. We show that the proposed method outperforms currently applied approaches and thereby provides an argument to tighten the intersection between computer science and social science, especially political science, further. We scrutinize the performance of our algorithm's runtime behavior to evaluate its applicability in the field. Numerical results with real data from a local election in the Austrian province of Styria from 2010 substantiate the applicability of the proposed approach.     

基于分形插值的改进趋势面拟合法在地面沉降中的应用

针对区域地面沉降监测点数量有限、分布不均的情况,一般采用空间插值的手段建立表面拟合模型来解决。基于分形插值,采用随机选择迭代函数的思想对传统的趋势面拟合法作出改进,改进后拟合优度系数提高0.03,达到0.995,且改进前后的拟合结果符合显著性检验的要求。实验结果表明:改进前后趋势面拟合法拟合结果满足地面沉降监测的精度要求,改进后方法的拟合优度更高,对现实地面沉降量变化的描绘更加真实,可为沉降灾害评价工作提供更准确的依据。     

Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

The paper presents a spatial analysis of points especially suited to estimate a preference map for new consumers, which is then used as an analytical tool in spatial electric load forecasting. This approach is an exploratory spatial data analysis used to discover useful point patterns in the spatial location of distribution transformers to calculate a preference value for each area, rating it with respect to a hypothetical load change that may occur. We consider the locations of distribution transformers occupied land. Random points are generated in the study area where the new loads are expected; these points are referred to as unoccupied land. The method uses a generalized additive model (GAM) to estimate the probability of unoccupied land becoming occupied land. We test the approach with data from a real distribution system in a mid-size city in Brazil; the result is a preference map that shows the areas where new consumers are most likely to be allocated. The main advantage of this method is the ability work with a small-scale resolution, which enables the use of a resolution suitable for spatial load forecasting method chosen. We test the calculated probabilities in a spatial load forecasting simulation, yielding results with lower spatial error when compared with the heuristic technique.     

基于VRS技术的煤矿地表变形监测实践研究

随着采深的增大,晋华宫矿开采过程产生严重的地表沉降问题,对该矿地面建构及设施造成了安全隐患。该矿选择采用虚拟参考站技术(VRS技术)来对地表沉降进行测量监测。监测结果表明,VRS技术具有更好的准确性和科学性,在监测过程中表现出测量范围广、工作效率高、工作量小的特点,可以对该矿地表沉降问题进行有效监测。     

一种改进的合成流量法及其在水文预报中的应用

合成流量法对下游站流量作出预报的关键是确定各上游站流量到达下游站的时间。在实际中常采用平均传播时间,然而不同水情下真实传播时间与平均传播时间存在一定的差距,影响预报精度。对此,提出时间窗口概念,以平均传播时间为中心,向前、后各开一个时间窗口,用上游各站时间窗口内流量的线性组合表示其到达下游站的流量,以提高模型对不同水情的适应能力和预报精度。以三峡水库2009~2015年流量数据率定参数,以2016~2018年流量数据作为预报对象,试验结果表明时间窗口法能够显著改善预报精度。