牛东晓
一直致力于电力工业领域中“多类型因素综合影响下的电力负荷预测理论方法”的教学与研究
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- 姓名:牛东晓
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学术头衔:
博士生导师, 教育部“新世纪优秀人才支持计划”入选者
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学科领域:
技术经济学
- 研究兴趣:一直致力于电力工业领域中“多类型因素综合影响下的电力负荷预测理论方法”的教学与研究
牛东晓,教授,博士生导师,华北电力大学经济与管理学院院长、MBA教育中心主任。
在工作中勤奋踏实,刻苦努力,做出了自己的贡献。1997年获得国务院政府特殊津贴, 2003年成为省部级重点学科“技术经济及管理”的学科带头人,2004年被聘为大学一级责任教授。2005年被聘为博士后导师,博士后流动站负责人。2007年获得“新世纪百千万人才工程国家级人选”、河北省第三届教学名师; 2008年“教育部新世纪优秀人才”,作为学科带头人获得“工商管理”国家级特色专业,2009年获得“首都教育先锋 教学创新先进个人”。
先后获得9项省部级成果奖励、7项其它成果奖励、5项荣誉奖励。负责和参加教学科研项目52项,其中:负责国家自然科学基金4项、教育部高校博士点专项基金1项、国家电网公司各类科研项目12项、大型电力工程评价项目5项,作为负责人的项目共29项,其余为其它科研项目。发表论文192篇,被SCI检索的论文11篇,被EI检索的论文86篇,被ISTP检索的论文24篇,国际期刊17篇,国际会议论文集76篇,出版论著5部。2009年《电力负荷预测》被河北省评为省级精品课。
一直致力于电力工业领域中“多类型因素综合影响下的电力负荷预测理论方法”的教学与研究,所研成果已应用到全国各地。同时也开展“可持续发展约束下电力工程综合评价理论方法”的教学与研究。
牛东晓院长工作敬业,在学院管理方面成绩显著,他进一步明确了学院的办学思路,提出“科学做规划、学科为先导、教学是基础、科研大突破、和谐出效益”的工作和管理理念,他带领班子成员在几年的工作中取得了突破性成果。
希望进一步学习兄弟学院的先进经验,取长补短,为创建国内一流、国际知名的经管学院而努力奋斗。
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【期刊论文】Power load forecasting using support vector machine and ant colony optimization
牛东晓, Dongxiao Niu a, Yongli Wang a, Desheng Dash Wu b, c, *
Expert Systems with Applications 37(2010)2531-2539,-0001,():
-1年11月30日
This paper creates a system for power load forecasting using support vector machine and ant colony optimization. The method of colony optimization is employed to process large amount of data and eliminate redundant information. The system mines the historical daily loading which has the same meteorological category as the forecasting day in order to compose data sequence with highly similar meteorological features. With this method, we reduced SVM training data and overcame the disadvantage of very large data and slow processing speed when constructing SVM model. This paper proposes a new feature selection mechanism based on ant colony optimization in an attempt to combat the aforemention difficulties. The method is then applied to find optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to complex systems monitoring, the ant colony optimization can mine the data more overall and accurate than the original fuzzy-rough method, an entropy-based feature selector, and a transformation-based reduction method, PCA. Comparing with single SVM and BP neural network in short-term load forecasting, this new method can achieve greater forecasting accuracy. It denotes that the SVM-learning system has advantage when the information preprocessing is based on data mining technology.
Ant colony optimization Feature selection Support vector machine Power load forecasting
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【期刊论文】A soft computing system for day-ahead electricity price forecasting
牛东晓, Dongxiao Niu a, Da Liu a, Desheng Dash Wu b, ∗
Applied Soft Computing xxx(2009)xxx-xxx,-0001,():
-1年11月30日
Hourly energy prices in a competitive electricity market are volatile. Forecast of energy price is key information to help producers and purchasers involved in electricity market to prepare their corresponding bidding strategies so as to maximize their profits. It is difficult to forecast all the hourly prices with only one model for different behaviors of different hourly prices. Neither will it get excellent results with 24 different models to forecast the 24 hourly prices respectively, for there are always not sufficient data to train the models, especially the peak price in summer. This paper proposes a novel technique to forecast day-ahead electricity prices based on Self-Organizing Map neural network (SOM) and Support Vector Machine (SVM) models. SOM is used to cluster the data automatically according to their similarity to resolve the problem of insufficient training data. SVM models for regression are built on the categories clustered by SOM separately. Parameters of the SVM models are chosen by Particle Swarm Optimization (PSO) algorithm automatically to avoid the arbitrary parameters decision of the tester, improving the forecasting accuracy. The comparison suggests that SOM-SVM-PSO has considerable value in forecasting day-ahead price in Pennsylvania-New Jersey-Maryland (PJM) market, especially forsummerpeak prices.
Electricity price forecasting Support Vector Machine (, SVM), Particle Swarm Optimization Self-Organizing Mapping (, SOM),
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【期刊论文】Middle-long power load forecasting based on particle swarm optimization☆
牛东晓, Dongxiao Niu a, Jinchao Li a, *, Jinying Li b, Da Liu a
Computers and Mathematics with Applications 57(2009)1883-1889,-0001,():
-1年11月30日
Middle-long forecasting of electric power load is crucial to electric investment, which is the guarantee of the healthy development of electric industry. In this paper, the particle swarm optimization (PSO) is used as a training algorithm to obtain the weights of the single forecasting method to form the combined forecasting method. Firstly, several forecasting methods are used to do middle-long power load forecasting. Then the upper forecasting methods are measured by several indices and the entropy method is used to form a comprehensive forecasting method evaluation index, following which the PSO is used to attain a combined forecasting method (PSOCF) with the best objective function value. We then obtain the final result by adding all the results of every single forecasting method. Taking actual load data of a power grid company in North China as a sample, the results show that PSOCF model improves the forecasting precision compared to the traditional models.
Power load forecasting Error index Entropy Particle swarm optimization
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【期刊论文】A New Short-term Power Load Forecasting Model Based on Chaotic Time Series and SVM
牛东晓, Dongxiao Niu, Yongli Wang, Chunming Duan, Mian Xing
Journal of Universal Computer Science, vol. 15, no.13 (2009), 2726-2745,-0001,():
-1年11月30日
This paper presents a model for power load forecasting using support vector machine and chaotic time series. The new model can make more accurate prediction. In the past few years, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on chaotic time series has been established. The time series matrix has also been established according to the theory of phase-space reconstruction. The Lyapunov exponents, one important component of chaotic time series, are used to determine time delay and embedding dimension, the decisive parameters for SVM. Then support vector machines algorithm is used to predict power load. In order to prove the rationality of chosen dimension, another two random dimensions are selected to compare with the calculated dimension. And to prove the effectiveness of the model, BP algorithm is used to compare with the results of SVM. Findings show that the model is effective and highly accurate in the forecasting of short-term power load. It means that the model combined with SVM and chaotic time series learning system have more advantage than other models.
Support vector machine,, Chaotic time series,, Lyapunov exponents,, Parameter selection,, Load forecasting
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牛东晓, Dong-Xiao Niu and Ying-Chun Guo ,
J. of Mult.-Valued Logic & Soft Computing, Vol. 00, pp. 1-18,-0001,():
-1年11月30日
Anovelsupportvectorregression(SVR)optimizedbyanimprovedparticle swarm optimization (PSO) combined with simulated annealing algorithm (SA) was proposed. The optimization mechanism also combined the discrete PSO with the continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVR kernel parameter setting. By incorporating with SA, the global searching capacity of the proposed model was enhanced. The improved SAPSO was used to optimize the parameters ofSVRand select the input features simultaneously. Based on the operational data provided by a regional power grid in north China, the method was used in short-term load forecasting (STLF). The experimental results showed the proposed approach can correctly select the discriminating input features and compared to the PSO-SVR and the traditional SVR, the average time of the proposed method in the experimental process reduced and the forecasting accuracy increased respectively. So, the improved method is better than the other two models.
Support vector regression (, SVR), ,, particle swarm optimization (, PSO), ,, simulated annealing (, SA), ,, parameter determination,, feature selection,, short-term load forecasting (, STLF), .,
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牛东晓, 赵磊, 张博, 王海峰
中国管理科学,2007,15(1):69~73,-0001,():
-1年11月30日
针对电力系统负荷特性,分析灰色模型GM(1,1) 的应用局限性,引入向量α改进灰色模型背景值序列的计算公式,从而构建了适应性更强的GM(1,1,α)模型。应用粒子群优化算法非线性全局寻优能力来求解最优α值,提出了基于粒子群优化算法的灰色模型PSOGM,并给出了电力负荷预测的应用实例。实例证明PSOGM模型具有较高的预测精度和较广的应用范围。
负荷预测, 灰色模型, 背景值, 粒子群优化
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牛东晓, 谷志红, 邢棉, 王会青
中国电机工程学报,2006,26(18):6~12,-0001,():
-1年11月30日
支持向量机方法已成功地应用在负荷预测领域,但它在训练数据时存在数据处理量太大、处理速度慢等缺点。为此提出了一种基于数据挖掘预处理的支持向量机预测系统,引用在处理大数据量、消除冗余信息等方面具有独特优势的数据挖掘技术,寻找与预测日同等气象类型的多个历史短期负荷,由此组成具有高度相似气象特征的数据序列,将此数据序列作为支持向量机的训练数据,可减少数据量,从而提高预测的速度和精度,克服支持向量机的上述缺点。将该系统应用于短期负荷预测中,与单纯的SVM方法和BP神经网络法相比,得到了较高的预测精度。
电力系统, 数据挖掘, 气象因素, 支持向量机, 短期负荷预测
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牛东晓, 刘达, 陈广娟, 冯义
电工技术学报,2007,22(6):148~153,-0001,():
-1年11月30日
利用支持向量机(SVM)和遗传算法(GA)建立24个不同的混合模型来对夏季24点负荷进行滚动预测。通过追加最新的负荷和天气信息来更新混合模型的输入,滚动预测下一小时负荷。利用SVM 建立预测模型,利用GA自动选择SVM模型的参数。经过GA优化后的最终SVM模型用于滚动预测下一小时的负荷。研究实例表明,GA简化了SVM参数选择,优化了SVM模型;滚动预测效果要明显好于常规预测方法。
支持向量机 小时负荷预测 遗传算法 滚动预测
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【期刊论文】A novel recurrent neural network forecasting model for power intelligence center
牛东晓, LIU Ji-cheng, NIU Dong-xiao
J. Cent. South Univ. Technol. (2008) 15: 726-732,-0001,():
-1年11月30日
In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power Intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.
load forecasting, uncertain element, power intelligence center, unascertained mathematics, recurrent neural network
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牛东晓, 李媛媛, 乞建勋, 刘达, 谷志红
中国电机工程学报,2008,28(16):96~102,-0001,():
-1年11月30日
提高预测精度需要准确把握负荷变化规律和环境因素影响,但目前的分析方法多存在依赖主观经验,且对因素影响分析不深入的问题。为此,提出一种基于经验模式分解(empirical mode decomposition,EMD)和因素影响的负荷分析新方法。利用EMD 的自适应性,自动地将目标负荷序列分解为若干个独立的内在模式,可克服依赖主观经验的缺点。再利用多个指标从不同方面分析它们的规律特性。通过各分量与各影响因素的相关分析,深入挖掘各因素对各分量基金项目:国家自然科学基金项目(70671039);高等学校博士点专项基金项目(20040079008)。Project Supported by National Natural Science Foundation of China(70671039),的影响情况。归纳出构成负荷的不同成分,并详细论述其特性。实例研究说明该方法可很好地分析负荷特性及因素影响。
电力负荷, 经验模式分解, 因素影响, 分析
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