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1.
为了对广东省的能源需求进行准确的预测,首先分析了影响广东省能源需求的各种因素,构建了预测指标体系.在此基础上,针对能源系统非线性等复杂系统特征,结合粒子群算法和BP神经网络的优点,构建了改进的PSO-BP神经网络的预测模型,并通过主成分分析法对指标体系进行数据降维,以降低神经网络的规模和复杂程度.以广东省1985-2013年的能源需求数据进行模拟与仿真,并对2014-2018年的能源需求量进行预测,理论分析和实证研究表明,该方法能够很好的反映广东省能源需求的特征,预测结果较为准确合理.  相似文献   

2.
针对猪肉价格上下波动呈非线性关系和影响因素复杂等难以预测的问题,提出了基于PCA-GM-BP神经网络预测模型对猪肉价格进行有效预测.以2010年1月-2018年12月的月度价格数据作为样本,共计108组数据,利用PCA对影响猪肉价格变化的12种因素进行降维处理,选用对猪肉价格的主要累积贡献率超过96%的5个主成分,构建PCA-GM-BP神经网络猪肉价格预测模型.结果表明:与传统的BP神经网络、GM-BP神经网络预测模型相比,PCA-GM-BP神经网络预测模型在提高聚类效果的同时,增加了预测结果的精确性,对我国猪肉价格预测具有更高的适用性与参考价值性.  相似文献   

3.
首先分析了影响广东省第三产业发展的主要因素,指出由于上述因素相互制约、相互影响,导致第三产业的发展呈现出高度的非线性特征,并使得单一的预测模型在预测效果和泛化能力方面难以胜任.在此基础上,提出了基于神经网络集成的组合预测模型,对广东省第三产业的发展进行预测,阐述了算法的基本原理和数据处理流程,实证分析表明:基于神经网络集成的组合预测模型要比单一预测模型的预测精度高.  相似文献   

4.
提出了一种在对预报因子集进行模糊聚类分析基础上构建径流预测模型的新方法:先通过模糊C-均值聚类将历史径流数据进行分类,然后利用小波神经网络分别建立预报因子集类别变量特征值与观测值之间的局部预测模型,并设计了特征值分类识别器,自动搜寻相适应的局部网络模型进行预测.通过西南某水库2011年日平均入库来流的计算实例对简单小波神经网络预测模型和所建的基于FCM与小波神经网络的预测模型进行了比较,结果较为满意.  相似文献   

5.
建立基于小波神经网络的预测模型,以不同时间滞差和影响因子组合作为输入变量,对海河流域四个监测断面的溶解氧浓度进行短期预测.结果表明,基于溶解氧历史数据的小波神经网络预测模型精度更高,可用于天然水体的水质预测,为水质管理提供更客观的参考和依据.  相似文献   

6.
杨进  陈亮 《经济数学》2018,(2):62-67
为了实现对股票价格变化的短期预测,提出了一种基于小波神经网络(WNN)与自回归积分滑动平均模型(ARIMA)的组合预测模型.将股票的收盘价序列数据划分为线性以及非线性(误差项)两个部分,分别利用统计学中ARIMA模型和小波神经网络分别对两部分数据进行预测并得到结果,将两部分结果组合相加合成为整个股票价格的预测结果.实验结果表明该组合模型在预测精度方面有提高,是一种比较有效的预测模型.  相似文献   

7.
在建立小波神经网络模型的基础上,提出了利用小波神经网络对高维非线性系统进行辨识的方法,得出了高维非线性系统的辨识算法,并通过实例仿真说明了系统的泛化能力得到有效提高,获得了具有良好自适应能力的小波网络.  相似文献   

8.
由于PM_(2.5)日均浓度值受外界多重复杂因素的影响,其较强的自相关性使得时间序列模型ARIMA构建难以实现,因此,给出高映射能力的非线性神经网络预测模型,并分别建立基于BP神经网络和GRNN神经网络的预测模型,进行PM_(2.5)浓度预测实验.结果表明,BP神经网络回检过程和检测过程存在不稳定性,预测残差波动较大,而GRNN神经网络检测残差呈完全U型,回检过程和检测过程较稳定,并且GRNN神经网络回检数据拟合度、预测数据精度和运算速度均优于BP神经网络,建模过程更为方便,易于实际应用.  相似文献   

9.
针对少数据、贫信息、非线性、动态性的时间序列,采用遗传算法对Elman神经网络的初始权值进行优化以避免陷入局部最小值.建立灰色GM(1,n)模型对其进行预测,使用优化后的神经网络对预测结果进行修正.通过实例拟合、预测,对比灰色GM(1,n)模型、灰色神经网络模型和基于遗传算法的灰色神经网络模型结果,验证预测模型的有效性.结果表明,基于遗传算法的灰色Elman神经网络预测模型能够扩大搜索范围,稳定网络结构,提高解的精度.  相似文献   

10.
为提高预测精度,解决非线性组合预测中的困难,利用改进BP神经网络对非线性组合预测模型进行了设计.讨论了模型设立的原则和一般程序,比较其与传统的组合预测方法之间的优劣,并给出实例加以验证.结果显示,基于改进BP神经网络的非线性组合预测模型能够准确描述系统中的非线性,提高预测精度.  相似文献   

11.
组合模型在我国能源需求预测中的应用   总被引:12,自引:0,他引:12  
文章首先比较了不同的能源需求预测方法的特点,并选择确定性加随机性时间序列组合模型对我国能源需求进行预测,然后详细介绍了建模的过程,并对模型预测精度和参数稳定性作了评价,结果表明本文采用的组合模型是一种比较有效的预测方法,最后用该模型对我国2004~2020能源需求进行了预测。  相似文献   

12.
组合预测模型在能源消费预测中的应用   总被引:4,自引:0,他引:4  
能源的需求预测是一个复杂的非线形系统,其发展变化具有增长性和波动性,组合预测对于信息不完备的复杂经济系统具有一定的实用性.本文利用我国能源消费的历史数据,采用灰色预测的GM(1,1)模型、BP神经网络模型和三次指数平滑模型进行优化组合,建立了能源消费组合预测模型,实证分析结果表明预测值和实际结果有很好的一致性,可以作为能源消费预测的有效工具.  相似文献   

13.
Grey forecasting models have taken an important role for forecasting energy demand, particularly the GM(1,1) model, because they are able to construct a forecasting model using a limited samples without statistical assumptions. To improve prediction accuracy of a GM(1,1) model, its predicted values are often adjusted by establishing a residual GM(1,1) model, which together form a grey residual modification model. Two main issues should be considered: the sign estimation for a predicted residual and the way the two models are constructed. Previous studies have concentrated on the former issue. However, since both models are usually established in the traditional manner, which is dependent on a specific parameter that is not easily determined, this paper focuses on the latter issue, incorporating the neural-network-based GM(1,1) model into a residual modification model to resolve the drawback. Prediction accuracies of the proposed neural-network-based prediction models were verified using real power and energy demand cases. Experimental results verify that the proposed prediction models perform well in comparison with original ones.  相似文献   

14.
Goal programming besides, being a broadly used decision tool, is shown in this paper to be a powerful forecasting device too. It is used as the main component of an energy demand forecasting system, performing a function of an integrator of information on the future growth of energy demand. This information comes from a variety of sources. The most important is a set of econometric models, based on different approaches, which explain past behaviour of the energy demand system. Other sources of information used are statements on government energy policy, feasibility or technical reports and, finally, the forecaster's guess. Thus GP is intended to relax many of the assumptions of conventional econometric forecasting, which render it inadequate under the prevailing conditions of quickly changing energy structures.An implementation of the system on the Greek economy is also described in this paper. Four scenarios of energy demand growth up to 1990 are studied. The levels of energy requirements in these scenarios vary as a consequence of the patterns of economic development and rates of increase in the price of oil.  相似文献   

15.
To achieve a competitive edge needed for marketing highly competitive products, modern enterprises have actively sought to provide the marketplace with an expansive range of products with high random volatility of demand and correlations between demands of product. Consequently, traditional forecasting methods for separately forecasting demand for these products are likely to yield significant deviations. Therefore, this study develops a real options approach-based forecasting model to accurately predict future demand for a given range of products with highly volatile and correlated demand. Additionally, this study also proposes using Monte Carlo simulation to solve the demand forecasting model. The real options approach associated with Monte Carlo simulation not only deals effectively with random variation involving a particular demand stochastic diffusion process, but can handle the correlations in product demand.  相似文献   

16.
Accurate demand forecasting is of vital importance in inventory management of spare parts in process industries, while the intermittent nature makes demand forecasting for spare parts especially difficult. With the wide application of information technology in enterprise management, more information and data are now available to improve forecasting accuracy. In this paper, we develop a new approach for forecasting the intermittent demand of spare parts. The described approach provides a mechanism to integrate the demand autocorrelated process and the relationship between explanatory variables and the nonzero demand of spare parts during forecasting occurrences of nonzero demands over lead times. Two types of performance measures for assessing forecast methods are also described. Using data sets of 40 kinds of spare parts from a petrochemical enterprise in China, we show that our method produces more accurate forecasts of lead time demands than do exponential smoothing, Croston's method and Markov bootstrapping method.  相似文献   

17.
为解决最小二乘支持向量机参数设置的盲目性,利用果蝇优化算法对其参数进行优化选择,进而构建了果蝇优化最小二乘支持向量机混合预测模型.以我国物流需求量预测为例,验证了该模型的可行性和有效性.实例验证结果表明:与单一最小二乘支持向量机和模拟退火算法优化最小二乘支持向量机预测模型相比,该模型不仅能够有效选择参数值,而且预测精度更高.  相似文献   

18.
短生命周期产品因为需求的随机性和产品价值的瞬间变化性,对预测准确性提出了更高的要求.然而许多企业在使用多种预测模型后发现其预测准确率并没有得到显著提升.以短生命周期产品需求特点为背景,在需求预测影响的BASS模型基础上,建立受生命周期和季节性因素影响的需求预测优化模型,最后通过一个产品的实例证实了验证了模型的合理性.  相似文献   

19.
Accurate short-term demand forecasting is critical for developing effective production plans; however, a short forecasting period indicates that the product demands are unstable, rendering tracking of product development trends difficult. Determining the actual developing data patterns by using forecasting models generated using historical observations is difficult, and the forecasting performance of such models is unfavourable, whereas using the latest limited data for forecasting can improve management efficiency and maintain the competitive advantages of an enterprise. To solve forecasting problems related to a small data set, this study applied an adaptive grey model for forecasting short-term manufacturing demand. Experiments involving the monthly demand data for thin film transistor liquid crystal display panels and wafer-level chip-scale packaging process data showed that the proposed grey model produced favourable forecasting results, indicating its appropriateness as a short-term forecasting tool for small data sets.  相似文献   

20.
Product life cycles have become increasingly shorter because of global competition. Under fierce competition, the use of small samples to establish demand forecasting models is crucial for enterprises. However, limited samples typically cannot provide sufficient information; therefore, this presents a major challenge to managers who must determine demand development trends. To overcome this problem, this paper proposes a modified grey forecasting model, called DSI–GM(1,1). Specifically, we developed a data smoothing index to analyze the data behavior and rewrite the calculation equation of the background value in the applied grey modeling, constructing a suitable model for superior forecasting performance according to data characteristics. Employing a test on monthly demand data of thin film transistor liquid crystal display panels and the monthly average price of aluminum for cash buyers, the proposed modeling procedure resulted in high prediction outcomes; therefore, it is an appropriate tool for forecasting short-term demand with small samples.  相似文献   

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