应用小波-人工神经网络组合模型研究电力负荷预报

针对负荷时间序列的非线性和多时间尺度特性．提出了将小波分析与人工神经网络相结合进行负荷预报的方法——小波-人工神经网络组合模型。该模型吸取了小波分析的多分辨功能和人工神经网络的非线性逼近能力。以月、日平均负荷预报为例对模型进行验证．结果表明：该模型的拟合、检验精度较高。     

基于自适应模糊推理系统模型的径流中长期预报

介绍了自适应模糊推理系统ANFIS的原理结构及学习算法。以漫湾和双牌两座水库实测月径流序列为研究对象，研究不同的输入及不同的模糊数对自适应模糊推理系统模型做中长期预报的影响，并通过与人工神经网络模型的预报结果进行比较，显示本模型是中长期水文预报方法中较为准确的方法之一。     

酒埠江水库入库流量预报的农步回归方法

基于物理成因分,挑选出影响酒埠江水库年、月平均入库流量的前期单站预报因子,并建立了逐步归预报模,实践表明,预报效果较好,可以在生产实际中应用。     

长江螺山站水位预报研究

运用BP人工神经网络建立了长江螺山站水位预报模型。选取模型影响因子时,用上游站点及本站的前期流量过程反映洪水涨落率、下游站点水位反映下游变动回水的顶托作用。为提高螺山站高水位时的预报精度,提出了以水位流量的时间差分值作为BP网络模型输入和输出的新方法。多个算例的计算结果表明,新方法能进一步提高预报精度,在洪水预报研究中具有推广应用价值。     

基于人工神经网络的钢铁冶炼终点预报模型

全面地综述了人工神经网络在“烧结-炼铁-炼钢(铁水预处理、转炉、电炉)-炉外精炼(LF精炼、RH精炼)”整个钢铁冶炼工艺过程的各生产环节中的终点成分和温度预报方面的开发应用工作。还简单讨论了人工神经网络与回归模型和专家系统的特性。人工神经网络技术在冶金过程终点预报应用方面具有广阔的应用前景。     

基于遗传程序设计的中长期径流预报模型研究与应用

应用遗传程序设计建立径流中长期预报模型,结合径流序列数据的特点通过自相关分析确定其滞时输入变量的个数,采用均方误差作为其适应度评价函数,以漫湾实测月径流序列(1953~2003年)和洪家渡实测月径流序列(1951~2004年)为例,通过与ARMA模型、人工神经网络模型的预报结果比较,显示该模型应用于径流中长期预报简单易行且精度较高。     

基于气象因子的汛期径流预报模型

针对现有汛期径流预报方法的缺陷,从物理成因出发,采用投影寻踪方法从74项大气环流因子中筛选出影响汛期总径流量的主因子,结合汛期前期降雨量进行相似分析获取相似年份,构建基于气象因子的汛期径流预报模型。以长江流域关键断面汛期来水预报为例开展了模型的实例研究,结果表明,该模型考虑了气象因子对长期径流变化的影响,对汛期总径流量的预报以及汛期月径流预报的精度均高于门限回归模型,是汛期径流预报的一种行之有效的方法。     

三峡水库中长期径流预报方法研究

中长期径流预报是充分利用水资源、发挥电站经济效益的有力手段。以三峡水库为研究对象,分别采用周期外延叠加技术、人工神经网络模型、投影寻踪自回归模型和支持向量机回归模型对三峡水库逐月入库径流进行预报。从不同侧面比较分析了这四种方法优劣,并总结各预报模型计算结果的特征及规律,为三峡水库寻求径流预报规律和制定未来中长期调度计划提供了技术支持。     

年径流变化的BP神经网络预报模型研究

针对现有基于线性方法的年径流预报模型预报精度不高的问题,利用乌江洪家渡1963~2016年径流系列资料,以5~10月月平均流量作为预报影响因子,构建以年径流量为预报对象的BP神经网络模型,形成6-11-1的网络结构,并选取泛化能力强的贝叶斯规则法TRAINBR为训练函数。模拟结果表明,模型预报效果良好,对于年径流预报具有实用价值;BP神经网络模型相比逐步线性回归方法能更精确表达年径流预报因子与预报对象的映射关系;采用的训练函数TRAINBR能有效改善模型的泛化能力。研究成果可为径流预报提供参考。     

线性回归法建立城市空气PM2.5预报模型——以宝鸡市为例

采用线性回归法建立宝鸡市秋冬季节降水量较少的情况下PM2.5的预报模式,对各影响因子的相关性作了分析。分析结果显示PM2.5浓度与前日PM2.5浓度、气压、温度和风速的相关性较好,与相对湿度的相关性较差。采用5因子模型预报和4因子预报模型分别对3月份的PM2.5浓度进行预报,4因子预报结果更接近实测值。宝鸡市PM2.5预报模型在冬春降水量偏少的情况下采用4因子预报模型更为准确。     

Predicting electricity energy consumption: A comparison of regression analysis,decision tree and neural networks

This study presents three modeling techniques for the prediction of electricity energy consumption. In addition to the traditional regression analysis, decision tree and neural networks are considered. Model selection is based on the square root of average squared error. In an empirical application to an electricity energy consumption study, the decision tree and neural network models appear to be viable alternatives to the stepwise regression model in understanding energy consumption patterns and predicting energy consumption levels. With the emergence of the data mining approach for predictive modeling, different types of models can be built in a unified platform: to implement various modeling techniques, assess the performance of different models and select the most appropriate model for future prediction.     

基于Elman神经网络的面板堆石坝沉降预测模型

分析了某面板堆石坝运行初期坝体的监测资料,选择水压分量与时效分量为影响因子构建逐步回归分析模型,应用反馈神经网络理论建立Elman神经网络模型,并与逐步回归模型预测精度做了对比分析.结果表明,Elman神经网络模型预测精度高、可靠,有助于分析大坝的安全性态.     

基于熵权法的组合模型在大坝渗流预测中的应用

针对大坝安全监控中单一预测模型预测精度较低的问题,基于多元线性回归预测模型和BP神经网络模型,应用熵原理提出一种新的线性组合预测模型,并结合某大坝渗透压力实际观测资料对该组合预测模型进行实用性检验。结果表明,短期内三种模型均具有较高的预测精度,但预测长度增加后,该组合预测模型预测精度更高。     

Short‐term electric load forecasting using an artificial neural network: case of Northern Vietnam

Short‐term electric load forecasting is an important requirement for electric system operation. This paper employs a feed‐forward neural network with a back‐propagation algorithm for three types of short‐term electric load forecasting: daily peak (valley) load, hourly load and the total load. The forecast has been made for the northern areas of Vietnam using a large set of data on peak load, valley load, hourly load and temperature. The data were used to train and calibrate the artificial neural network, and the calibrated network was used for load forecasting. The results obtained from the model show that the application of neural network to short‐term electric load forecasting problem is very useful with quite accurate results. These results compare well with other similar studies. Copyright © 2004 John Wiley & Sons, Ltd.     

Support vector machines for short‐term electrical load forecasting

Short‐term electrical load forecasting plays a vital role in the electric power industries. It ensures the availability of supply of electricity, as well as providing the means of avoiding over‐ and under‐utilization of generating capacity and therefore optimizes energy prices. Several methods have been applied to short‐term load forecasting, including statistical, regression and neural networks methods. This paper introduces support vector machines, the latest neural network algorithm, to short‐term electrical load forecasting and compares its performance with the auto‐regression model. The results indicate that support vector machines compare favourably against the auto‐regressive model using the same data for building and testing both models based on the root‐mean‐square errors between the actual and the predicted data. Support vector machines allow the training data set to be increased beyond what is possible using the auto‐regressive model or other neural networks methods. Increasing the training data further improves the performance of support vector machines method. Copyright © 2002 John Wiley & Sons, Ltd.     

基于长短期记忆神经网络的地表太阳辐照度预测

针对地表太阳辐照度(GHI)短期预测问题,提出一种基于长短期记忆神经网络的短期太阳辐照度预测模型。采用递归结构的训练样本,以保证训练样本内部的时间耦合性。为验证所提模型预测GHI的有效性,采用算例与传统人工神经网络模型预测结果进行对比分析。结果表明:基于长短期记忆神经网络预测模型将均方误差降低88.48%,表明所建模型更适用于GHI预测。     

Short term and medium term power distribution load forecasting by neural networks

Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey.