电力系统负荷区间预测
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摘要
电力系统负荷预测是电力系统规划、计划、营销、市场交易、调度等部门的重要依据,其重要性早已被人们所认识。长期以来,国内外学者和电力系统运行管理专家不断探索,形成了一系列行之有效的预测方法。但分析现有的负荷预测方法发现,大量方法所得到的都是确定性的负荷预测结果。实际上,由于电力系统中蕴含了各种不确定因素,使得决策工作必然面临一定程度的风险,所以在决策时必须考虑电力需求的不确定性。传统确定性预测方法的结果不能反映需求的不确定性,而区间预测可满足这种客观要求。区间预测的结果不是一个简单的确定性数值,而是一个区间,并且这个区间对应了一定水平的概率置信水平,能描述未来预测结果的可能范围。根据区间预测结果,电力系统决策人员在进行生产计划、系统安全分析等工作时能够更好地认识到未来负荷可能存在的不确定性和面临的风险因素,从而及时作出更为合理的决策。因此,分析电力系统负荷的变化规律,研究电力负荷区间预测方法,实现电力负荷的不确定性预测具有重要的理论意义和实用价值。
本文通过对中长期电力负荷与短期电力负荷的特性分析,识别负荷自身变化以及相关因素的影响规律,采用灰色系统理论、神经网络模型和混沌时间序列方法,对电力负荷区间预测的模型与方法进行了研究。通过实例验证,区间预测结果具有较好的精度,证明了区间预测算法的有效性,研究成果可应用于电力市场分析与预测系统中,为电力系统运行管理提供科学的决策依据。主要研究工作和创新性成果如下:
(1)对于中长期电力负荷预测,针对传统灰色模型GM(1,1)在预测非指数型发展序列时存在误差过大的缺陷,将非线性灰色Bernoulli模型应用于负荷预测中,并提出了基于粒子群优化的参数优选方法。通过不同发展规律序列的测试数据以及实际电网负荷数据的预测结果表明,非线性灰色Bernoulli模型在适应性与预测精度等方面,较传统的GM(1,1)模型与灰色Verhulst模型有不同程度的改善。为进行区间预测,针对中长期负荷预测存在影响因素较多的特点,采用线性回归模型;而考虑到缺少相关因素历史数据的问题,则建立了一元线性回归与灰色模型相结合的组合预测模型。通过福建省年度用电量的预测结果表明,组合预测方法是非常有效的。
(2)分析天气等因素对短期负荷变化的影响规律,针对传统模糊聚类分析方法在处理温度等天气变量时转化为确定值存在信息丢失的问题,引入基于区间值的模糊聚类处理方法。区间值模糊聚类方法用区间值表示各个对象对于每个因素的隶属度,在区间层次上求各个对象之间的相似度,最终获得聚类结果。根据区间模糊聚类结果选择学习样本,采用区间运算反向传播(IABP)学习算法,建立了负荷预测的IABP神经网络模型。该模型充分发挥了区间运算和模糊理论处理不确定性问题的能力以及神经网络处理非线性问题的优势,可用区间变量作为输入,网络输出作为区间预测结果,给出了未来负荷的变化范围。
(3)根据非线性动力系统理论进行负荷建模和预测,将预测精度作为辨识工具,识别电力负荷自身变化的动力特性。研究结果表明,负荷的变化特性可以描述为低维混沌系统。针对负荷的混沌特性及向前一步预测的精度提出了一种优选相空间重构参数的方法,并采用加权一阶局域法多步预测模型进行了负荷预测。通过相空间重构能识别负荷序列的内部特性并进行预测,因此相空间重构是分析和预测负荷的有效工具。
(4)根据短期电力负荷变化的混沌特性,同时避免确定性混沌预测方法中存在着如嵌入维数、延迟时间及相似数据提取方法等一些未定因素带来的误差,从区间预测的角度提出了一种电力负荷混沌区间预测方法。该方法首先进行相空间重构,采用聚类算法在相空间中寻找当前时刻相点的相似状态,根据不同相似状态的预测结果确定未来负荷的取值区间,并根据历史预测误差的统计规律计算预测区间对应的概率置信水平。采用北方某电网负荷数据进行了实验研究,验证了该方法的可行性与有效性。
(5)概率性预测可以建立任意置信水平的区间预测结果,本文在混沌负荷序列确定性预测结果的基础上,基于局部预测方差,提出了一种短期负荷概率性预测的混沌时间序列方法。首先通过混沌时间序列预测方法得到不同相似状态的确定性预测结果,进一步计算局部预测方差,并由分位数估计得到历史预测误差样本分布规律。根据局部预测方差与分位数估计,结合确定性预测结果构造预测区间,得到概率性预测结果。
It’s been long-termly recognized that power load forecasting is important for many power system departments such as designing, planning, programming, marketing, trading, scheduling and so on. Through a constant exploratory work of domestic and foreign scholars along with experts in power system operation and management for a long time, a series of effective forecasting methods have been developed. However, analysis of the existing load forecasting methods finds that a large number of methods get deterministic forecasting results. In fact, decision-making inevitably has a certain degree of risk because of the various uncertainty factors in power system; therefore, the uncertainty of power demand must be taken into account in decision-making. The outcome of the traditional deterministic forecasting methods cannot reflect the uncertainty of the demand while that of the interval forecasting method is able to meet this objective requirement. The interval forecasting method does not offer a simple determinate forecast, but a range to describe the possible trend of future forecasting result, corresponding to a certain probability confidence level.. According to the results of the interval forecast, the power system decision-makers can make a better understand of the fluctuations and the possible uncertainties of the future load as well as the risk factors it would face, so as to make more reasonable decisions timely. Therefore, it's of great practical and theoretical significance to analyze the power load variation law and study the power load probabilistic forecasting method to realize the power load uncertain prediction.
In this paper, on the base of the characteristic analysis of the long-term and short-term power load, along with the identification of the load itself variation and the influence rules of relevant factors, the power load interval forecast models and its solving methods are studied using grey system theory, neural network models and chaotic time series method. The examples verify the accuracy of the interval forecasting results and prove the effectiveness of the algorithm. The research achievements can be applied to the electricity market analysis and forecast system to provide a scientific basis for decision making in power system operation and management. The main research and innovative results are as follows:
(1) The traditional grey model GM(1,1) often has great error when forecasting the non-exponential growth curve. In order to solve this problem, the nonlinear gray Bernoulli model (NGBM) is applied to medium- and long-term power load forecasting and a particle swarm optimization (PSO) algorithm is proposed to optimize the parameter of NGBM. Through the verification using different testing data and the forecasting of power load data in actual power system, it is proved that the proposed method possesses better adaptability and higher forecasting accuracy than traditional GM(1,1) and Grey Verhulst model. According to the fact that many factors affect the load, simple linear regression and multiple linear regression were employed to interval load forecasting. And considering lack of history data of related factors, a novel combined method based on simple linear regression and GM(1,1) is used. The interval forecasting results of Fujian province’s load demand show that the combined method has a better forecasting effect.
(2) Aiming at solving the information loss problem when converting the weather variables to the determinate value in traditional fuzzy clustering analysis method, based on the analysis of the influence law of weather and day type on the short-term load, a new clustering analysis method using interval value is presented.The new method uses the interval value to describe the membership degree of every element in the classification set,and then try to get the similarity of intervals and finally the aggregation.The learning samples are selected by the new fuzzy clustering method and a load forecasting model using the interval arithmetic back-propagation neural network (IABPNN) is established. This model can fully develop the ability of solving uncertainty problem by interval computation and fuzzy theory and the ability of solving nonlinear problems by neural network. It takes the interval value as the input, network outcome as the interval forecasting results, to give the changing range of future power load.
(3) Nonlinear dynamical system theory is applied to the modeling and prediction of power load. Prediction accuracy is selected as an identification tool to analyze dynamic characteristics of power load variation. Analysis results of load time series show that the variation of power load can be characterized as a low-dimensional chaotic system. According to chaotic characteristic of power load and the accuracy of one-step forward prediction, the authors propose a new method to implement optimal selection of reconstruction parameters, such as the best embedding dimension and delay time, and use weighted local-region multi-step forecasting model based on phase-space reconstruction to forecast short-term load. Because phase space model can identify the inherent characteristics of power load and can be used in load forecasting, the proposed method is effective in power load analysis and forecasting.
(4) According to the chaotic characteristic of power load, a chaotic time series algorithm for short-term load probabilistic interval forecasting is proposed to avoid the error caused by embedding dimension, time delay and similar states extracted method in determinate chaotic forecasting method. First, reconstruct the phase space in the way of searching similar states of current phase point using the clustering algorithm, and determine the interval of the future load values according to the forecasting results of the similar states. Meanwhile, calculate the corresponding probability of the interval on the base of the statistical characters of history forecasting error. The feasibility and effectiveness of the proposed method is evaluated by applying it to a northern power grid.
(5) Probabilistic forecasting provides more information than interval forecasting.. In order to meet the demands of uncertain risk analysis and decision-making in electricity market, a probabilistic load forecasting method based on chaotic time series forecasted method is presented. First, the deterministic forecasting results and local predictive variance are obtained using chaotic time series method, and then the distribution and the percentiles of history load forecasting errors is estimated. According to the estimation of the percentiles and local predictive variance, along with the combination of the deterministic load forecasting result, the forecasting interval is constructed and the probabilistic load forecasting results can be obtained. The practicability and validity of the proposed method are tested with the actual data.
本文通过对中长期电力负荷与短期电力负荷的特性分析,识别负荷自身变化以及相关因素的影响规律,采用灰色系统理论、神经网络模型和混沌时间序列方法,对电力负荷区间预测的模型与方法进行了研究。通过实例验证,区间预测结果具有较好的精度,证明了区间预测算法的有效性,研究成果可应用于电力市场分析与预测系统中,为电力系统运行管理提供科学的决策依据。主要研究工作和创新性成果如下:
(1)对于中长期电力负荷预测,针对传统灰色模型GM(1,1)在预测非指数型发展序列时存在误差过大的缺陷,将非线性灰色Bernoulli模型应用于负荷预测中,并提出了基于粒子群优化的参数优选方法。通过不同发展规律序列的测试数据以及实际电网负荷数据的预测结果表明,非线性灰色Bernoulli模型在适应性与预测精度等方面,较传统的GM(1,1)模型与灰色Verhulst模型有不同程度的改善。为进行区间预测,针对中长期负荷预测存在影响因素较多的特点,采用线性回归模型;而考虑到缺少相关因素历史数据的问题,则建立了一元线性回归与灰色模型相结合的组合预测模型。通过福建省年度用电量的预测结果表明,组合预测方法是非常有效的。
(2)分析天气等因素对短期负荷变化的影响规律,针对传统模糊聚类分析方法在处理温度等天气变量时转化为确定值存在信息丢失的问题,引入基于区间值的模糊聚类处理方法。区间值模糊聚类方法用区间值表示各个对象对于每个因素的隶属度,在区间层次上求各个对象之间的相似度,最终获得聚类结果。根据区间模糊聚类结果选择学习样本,采用区间运算反向传播(IABP)学习算法,建立了负荷预测的IABP神经网络模型。该模型充分发挥了区间运算和模糊理论处理不确定性问题的能力以及神经网络处理非线性问题的优势,可用区间变量作为输入,网络输出作为区间预测结果,给出了未来负荷的变化范围。
(3)根据非线性动力系统理论进行负荷建模和预测,将预测精度作为辨识工具,识别电力负荷自身变化的动力特性。研究结果表明,负荷的变化特性可以描述为低维混沌系统。针对负荷的混沌特性及向前一步预测的精度提出了一种优选相空间重构参数的方法,并采用加权一阶局域法多步预测模型进行了负荷预测。通过相空间重构能识别负荷序列的内部特性并进行预测,因此相空间重构是分析和预测负荷的有效工具。
(4)根据短期电力负荷变化的混沌特性,同时避免确定性混沌预测方法中存在着如嵌入维数、延迟时间及相似数据提取方法等一些未定因素带来的误差,从区间预测的角度提出了一种电力负荷混沌区间预测方法。该方法首先进行相空间重构,采用聚类算法在相空间中寻找当前时刻相点的相似状态,根据不同相似状态的预测结果确定未来负荷的取值区间,并根据历史预测误差的统计规律计算预测区间对应的概率置信水平。采用北方某电网负荷数据进行了实验研究,验证了该方法的可行性与有效性。
(5)概率性预测可以建立任意置信水平的区间预测结果,本文在混沌负荷序列确定性预测结果的基础上,基于局部预测方差,提出了一种短期负荷概率性预测的混沌时间序列方法。首先通过混沌时间序列预测方法得到不同相似状态的确定性预测结果,进一步计算局部预测方差,并由分位数估计得到历史预测误差样本分布规律。根据局部预测方差与分位数估计,结合确定性预测结果构造预测区间,得到概率性预测结果。
It’s been long-termly recognized that power load forecasting is important for many power system departments such as designing, planning, programming, marketing, trading, scheduling and so on. Through a constant exploratory work of domestic and foreign scholars along with experts in power system operation and management for a long time, a series of effective forecasting methods have been developed. However, analysis of the existing load forecasting methods finds that a large number of methods get deterministic forecasting results. In fact, decision-making inevitably has a certain degree of risk because of the various uncertainty factors in power system; therefore, the uncertainty of power demand must be taken into account in decision-making. The outcome of the traditional deterministic forecasting methods cannot reflect the uncertainty of the demand while that of the interval forecasting method is able to meet this objective requirement. The interval forecasting method does not offer a simple determinate forecast, but a range to describe the possible trend of future forecasting result, corresponding to a certain probability confidence level.. According to the results of the interval forecast, the power system decision-makers can make a better understand of the fluctuations and the possible uncertainties of the future load as well as the risk factors it would face, so as to make more reasonable decisions timely. Therefore, it's of great practical and theoretical significance to analyze the power load variation law and study the power load probabilistic forecasting method to realize the power load uncertain prediction.
In this paper, on the base of the characteristic analysis of the long-term and short-term power load, along with the identification of the load itself variation and the influence rules of relevant factors, the power load interval forecast models and its solving methods are studied using grey system theory, neural network models and chaotic time series method. The examples verify the accuracy of the interval forecasting results and prove the effectiveness of the algorithm. The research achievements can be applied to the electricity market analysis and forecast system to provide a scientific basis for decision making in power system operation and management. The main research and innovative results are as follows:
(1) The traditional grey model GM(1,1) often has great error when forecasting the non-exponential growth curve. In order to solve this problem, the nonlinear gray Bernoulli model (NGBM) is applied to medium- and long-term power load forecasting and a particle swarm optimization (PSO) algorithm is proposed to optimize the parameter of NGBM. Through the verification using different testing data and the forecasting of power load data in actual power system, it is proved that the proposed method possesses better adaptability and higher forecasting accuracy than traditional GM(1,1) and Grey Verhulst model. According to the fact that many factors affect the load, simple linear regression and multiple linear regression were employed to interval load forecasting. And considering lack of history data of related factors, a novel combined method based on simple linear regression and GM(1,1) is used. The interval forecasting results of Fujian province’s load demand show that the combined method has a better forecasting effect.
(2) Aiming at solving the information loss problem when converting the weather variables to the determinate value in traditional fuzzy clustering analysis method, based on the analysis of the influence law of weather and day type on the short-term load, a new clustering analysis method using interval value is presented.The new method uses the interval value to describe the membership degree of every element in the classification set,and then try to get the similarity of intervals and finally the aggregation.The learning samples are selected by the new fuzzy clustering method and a load forecasting model using the interval arithmetic back-propagation neural network (IABPNN) is established. This model can fully develop the ability of solving uncertainty problem by interval computation and fuzzy theory and the ability of solving nonlinear problems by neural network. It takes the interval value as the input, network outcome as the interval forecasting results, to give the changing range of future power load.
(3) Nonlinear dynamical system theory is applied to the modeling and prediction of power load. Prediction accuracy is selected as an identification tool to analyze dynamic characteristics of power load variation. Analysis results of load time series show that the variation of power load can be characterized as a low-dimensional chaotic system. According to chaotic characteristic of power load and the accuracy of one-step forward prediction, the authors propose a new method to implement optimal selection of reconstruction parameters, such as the best embedding dimension and delay time, and use weighted local-region multi-step forecasting model based on phase-space reconstruction to forecast short-term load. Because phase space model can identify the inherent characteristics of power load and can be used in load forecasting, the proposed method is effective in power load analysis and forecasting.
(4) According to the chaotic characteristic of power load, a chaotic time series algorithm for short-term load probabilistic interval forecasting is proposed to avoid the error caused by embedding dimension, time delay and similar states extracted method in determinate chaotic forecasting method. First, reconstruct the phase space in the way of searching similar states of current phase point using the clustering algorithm, and determine the interval of the future load values according to the forecasting results of the similar states. Meanwhile, calculate the corresponding probability of the interval on the base of the statistical characters of history forecasting error. The feasibility and effectiveness of the proposed method is evaluated by applying it to a northern power grid.
(5) Probabilistic forecasting provides more information than interval forecasting.. In order to meet the demands of uncertain risk analysis and decision-making in electricity market, a probabilistic load forecasting method based on chaotic time series forecasted method is presented. First, the deterministic forecasting results and local predictive variance are obtained using chaotic time series method, and then the distribution and the percentiles of history load forecasting errors is estimated. According to the estimation of the percentiles and local predictive variance, along with the combination of the deterministic load forecasting result, the forecasting interval is constructed and the probabilistic load forecasting results can be obtained. The practicability and validity of the proposed method are tested with the actual data.
引文
[1]康重庆,夏清,刘梅.电力系统负荷预测.北京:中国电力出版社,2007.
[2]康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨.电力系统自动化,2004,28(17):1~11.
[3]张峰.电力负荷管理技术.北京:中国电力出版社,2005.
[4]刘晨晖.电力系统负荷预报理论及方法.哈尔滨:哈尔滨工业大学出版社,1987.
[5]牛东晓,曹树华,赵磊,等.电力负荷预测技术及其应用.北京:中国电力出版社,1998.
[6] Ranaweera D K, Karady G G, Farmer R G. Economic impact analysis of load forecasting. IEEE Transactions on Power Systems, 1997, 12(3): 1388~1392.
[7] Douglas A P, Breipohl A M, Lee F N, et al. Risk due to load forecast uncertainty in short term power system planning. IEEE Transactions on Power Systems, 1998, 13(4): 1493~1499.
[8]赵希正.中国电力负荷特性分析与预测.北京:中国电力出版社,2002.
[9]肖国泉,王春,张福伟.电力负荷预测.北京:中国电力出版社,2001.
[10]张伏生,刘芳,赵文斌,等.灰色Verhulst模型在中长期负荷预测中的应用.电网技术,2003,27(5):37~39,81.
[11]曹国剑,黄纯,隆辉,等.基于GM(1,1)改进模型的电网负荷预测.电网技术,2004,28(13):50~53.
[12]余健明,燕飞,杨文宇,等.中长期电力负荷的变权灰色组合预测模型.电网技术,2005,29(17):26~29.
[13]张大海,史开泉,江世芳.灰色负荷预测的参数修正法.电力系统及其自动化学报,2001,4(2):20~22.
[14]朱芸,乐秀璠.可变参数无偏灰色模型的中长期负荷预测.电力自动化设备,2003,23(4):25~27.
[15]顾洁,申刚,徐光虎.改进的电力系统中长期负荷预测方法研究.电力自动化设备,2002,22(6):1~4.
[16]王吉权,赵玉林.生长曲线在电力负荷预测中的应用.电网技术,2004,28(22):36~39.
[17]宁波,康重庆,夏清.中长期负荷预测模型的扩展策略.中国电力,2000,33(10):36~38.
[18]刘遵敬.最小概率最大化回归方法在电力负荷中期预测中的应用.中国电力,2004,37(9):50~54.
[19]陈柔伊,张尧,武志刚,等.改进的模糊聚类方法在负荷预测中的应用.电力系统及其自动化学报,2005,17(3):73~77.
[20]伍力,吴捷,叶军.负荷中长期预测中一种改进的模糊聚类算法.电网技术,2000,24(1):36~38.
[21]顾洁.电力系统中长期负荷预测的模糊算法.上海交通大学学报,2002,36(2):255~258.
[22]杨宗麟,吴德伟.利用“计量经济模型”预测电力负荷.华东电力,1997(3):1~5.
[23]韦凌云,吴捷,刘永强.基于系统动力学的电力系统中长期负荷预测.电力系统自动化,2000,24(16):44~47.
[24]钟庆,吴捷,伍力,等.基于系统动力学的分区负荷预测.电网技术,2001,25(3):51~55.
[25]谢敬东,唐国庆,徐高飞,等.组合预测方法在电力负荷预测中的应用.中国电力,1998,31(6):3~5.
[26]康重庆,夏清,沈渝,等.电力系统负荷预测的综合模型.清华大学学报,1999,39(1):8~11.
[27]余健明,燕飞,杨文宇,等.中长期电力负荷的变权灰色组合预测模型.电网技术,2005,29(17):26~29.
[28] Chen G. J, Li K K, Chung T S, et al. Application of an innovative combined forecasting method in power system load forecasting, Electric Power Systems Research, 2001, 59: 131~137.
[29]赵海清,牛东晓.负荷预测的交叉式自适应优选组合预测模型.华北电力大学学报,2000,27(4):13~17.
[30]高峰,康重庆,夏清,等.负荷预测中多模型的自动筛选方法.电力系统自动化,2004,28(6):11~13,40.
[31] Charytoniuk W, Chen M S, Kotas P, et al. Demand forecasting in power distribution systems using nonparametric probability density estimation. IEEE Transactions on Power Systems, 1999, 14(4):1200~1206.
[32]刘健,徐精求,董海鹏.配电网概率负荷分析及其应用.电网技术,2004,28(6):67~75.
[33]康重庆,杨高峰,夏清.电力需求的不确定性分析.电力系统自动化,2005,29(17):14~19.
[34]杨高峰,康重庆,徐国新,等.多维序列运算理论及其在不确定性电力需求分析中的应用.中国电机工程学报,2005,25(25):12~17.
[35]康重庆.序列运算理论及其应用.北京:清华大学出版社,2003.
[36] Alfares H K, Nazeeruddin M. Electric load forecasting: literature survey and classification of methods. International Journal of Systems Science, 2002, 33(1): 23~34.
[37]尚金成,黄永晧,夏清,等.电力市场理论研究与应用.北京:中国电力出版社,2002.
[38] Papalexopoulos A D, Hesterberg T C. A regression-based approach to short-term system load forecasting, IEEE Transactions on Power Systems 1990, 5(4): 1535~1547.
[39]王士政.电网调度自动化与配网自动化技术(第二版).北京:中国水利水电出版社,2006.
[40]田忠林.短期负荷预测自回归动平均模型的辨识方法.东北电力技术,1999,11:6~9.
[41]叶玫昀,罗耀华,刘勇,等.基于ARMA模型的电力系统短期负荷预测方法研究.信息技术,2002,6:74~76.
[42] Shy-Jier Huang, Kuang-Rong Shih. Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Transactions on Power Systems, 2003, 18(2): 205~215.
[43] N. Amjady, Short-term hourly load forecasting using time-series modeling with peak load estimation capability, IEEE Transactions on Power Systems, 2001, 16(3): 498~505.
[44] Chen J F, Wang W M, Huang C M. Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting, Electric Power Systems Research, 1995, 34: 187~196.
[45]顾洁.应用小波分析进行短期负荷预测.电力系统及其自动化学报,2003,15(2):40~65.
[46] Tai N, Jurgen S, Wu H X. Techniques of applying wavelet transform into combined model for short-term load forecasting. Electric Power Systems Research, 2006, 76(6-7): 525~533.
[47]王家红,黄阿强,熊信艮.基于小波网络的短期负荷预测方法.电力自动化设备,2003,23(3):11~12.
[48]李天云,刘自发.电力系统负荷的混沌特性及预测.中国电机工程学报,2000,20(11):36~40.
[49]吕金虎,占勇,陆君安.电力系统短期负荷预测的非线性混沌改进模型.中国电机工程学报,2000,20(12):80~83.
[50]蒋传文,权先璋,李承军,等.混沌理论在电力系统负荷预测中的应用.武汉交通科技大学学报,1999,23(6):608~611.
[51]温权,张勇传,程时杰.负荷预报的混沌时间序列分析法.电网技术,2001,25(10):13~16.
[52]梁志珊,王丽敏,付大鹏.应用混沌理论的电力系统短期负荷预测.控制与决策,1998,13(1):87~94.
[53]雷绍兰,孙才新,刘凡,等.电力短期负荷的混沌局域关联性预测.重庆大学学报(自然科学版),2005,28(5):24~28.
[54] Choi J G, Park J K, Kim K H, et al. Daily peak load forecasting system using a chaotic time series.in: Proceedings of the International Conference on Intelligent Systems Applications to Power Systems. Orlando, USA, 1996, 283~287.
[55] Arango H G, de Souza A C Z, Lambert-Torres G, et al. Difference between regular and deterministic chaos processes based on time analysis of load: an example using CEMIG data. Electric Power Systems Research, 2000, 56(1): 35~41.
[56] Mori H, Urano S. Short-term load forecasting with chaos time series analysis. in: Proceedings of the International Conference on Intelligent Systems Applications to Power Systems (SAP’96). 1996, 133~137.
[57]许涛,贺仁睦,王鹏,等.基于输入空间压缩的短期负荷预测.电力系统自动化,2004,28(6):51~56.
[58] Ruzic S, Vuckovic A, Nikolic N. Weather sensitive method for short-term load forecasting in electric power utility of Serbia. IEEE Transactions on Power Systems, 2003, 18(4): 1581~1586.
[59]汪峰,于尔铿,阎承山,等.基于因素影响的电力系统短期负荷预报方法的研究.中国电机工程学报,1999,19(8):54~58.
[60]张国江,邱家驹,李继红.基于模糊推理系统的多因素电力负荷预测.电力系统自动化,2002,26(5):49~53.
[61]康重庆,程旭,夏清,等.一种规范化的处理相关因素的短期负荷预测策略.电力系统自动化,1999,23(18):32~35.
[62]胡阳.考虑气象因素短期负荷预测的理论与算法的研究.北京:清华大学,1998.
[63] Park D C, EI-Sharkawi M A, Marks R J. Electric load forecasting using artificial neural network. IEEE Transactions on Power Systems, 1991, 6(2): 442~449.
[64] Lee K Y, Cha Y T, Park J H. Short term load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 1992, 7(1): 124~130.
[65] Mori H, Yuibara A. Deterministic annealing clustering for ANN-based short-term load forecasting. IEEE Transactions on Power Systems, 2001, 16(3): 545~551.
[66] Chang-il K, In-keun Y, Song Y H. Kohonen neural network and wavelet transform based approach to short-term load forecasting. Electric Power Systems Research, 2002, 63(2): 169~176.
[67] Alsayegh O A. Short-term load forecasting using seasonal artificial neural networks. International Journal of Power and Engery Systems, 2003, 23(3): 137~142.
[68] Kiartzis S J, Bakirtzis A G., Petridis V. Short-term load forecasting using neural networks. Electric Power Systems Research, 1995, 33:1~6.
[69] Ranaweera D K, Hubele N F, Paplexopoulos A D. Application of radial basis function neural network model for short-term load forecasting. IEE Proceedings- Generation Transmission and Distribution, 1995, 142(1): 45~50.
[70]高山,单渊达.基于径向基函数网络的短期负荷预测.电力系统自动化,1999,23(5):31~34.
[71]彭建春,江荣汉.电力系统短期负荷预报的动态神经网络方法.湖南大学学报,1997,24(1):83~86.
[72] AlFuhaid A S, El-Sayed M A, Mahmoud M S, Cascaded artificial neural networks for short-term load forecasting, IEEE Transactions on Power Systems, 1997, 12(4):1524~1529.
[73] Chen S T, Yu D C, Moghaddamjo A R. Weather sensitive short-term load forecasting using nonfully connected artificial neural network. IEEE Transactions on Power Systems, 1992, 7(3): 1098~1105.
[74] Ho K L, Hsu Y-Y, Yang C-C. Short-term load forecasting using a multilayer neural network with an adaptive learning algorithm. IEEE Transactions on Power systems, 1992, 7(1):141~149.
[75]丁军威,孙雅明.基于混沌学习算法的神经网络短期负荷预测.电力系统自动化,2000,24(2):32~35.
[76]王民量,张伯明,夏清.电力系统短期负荷预测的共轭梯度ANN方法.电力系统自动化,1999,23(1):34~36.
[77] Srinivasan D. Evolving artificial neural networks for short term load forecasting. Neurocomputing, 1998, 23(1): 265~276.
[78] Bakirtzis A G, Theocharis J B, Kiartzis S J, et al. Short-term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 1995, 10(3):1518~1524.
[79]牛东晓,邢棉,谢宏,等.短期电力负荷预测的小波神经网络模型的研究.电网技术,1999,23(4):21~24.
[80]孙雅明,张智晟.相空间重构和混沌神经网络融合的短期负荷预测研究.中国电机工程学报,2004,24(1):44~48.
[81]岑文辉,雷友坤,谢恒.应用人工神经网络与遗传算法进行短期负荷预测.电力系统自动化,1997,21(3):29~32.
[82]陈耀武,汪乐宇,龙洪玉.基于组合式神经网络的短期电力负荷预测模型.中国电机工程学报,2001,21(4):79~82.
[83]甘文泉,胡保生.用自适应神经元网络进行短期电力负荷预测.电网技术,1997,21(3):28~31.
[84]贺蓉,曾刚,姚建刚,等.天气敏感性神经网络在地区电网短期负荷预测中的应用.电力系统自动化,2001,25(17):32~35,52.
[85]姚李孝,宋玲芳,李庆宇等.基于模糊聚类分析与BP网络的电力系统短期负荷预测.电网技术,2005,29(1):20~23.
[86]姜勇.基于模糊聚类的神经网络短期负荷预测方法.电网技术,2003,27(2):45~49.
[87] Srinivasan D, Swee S T, Chang C S, et al. Parallel neural network-fuzzy Expert system strategy for short-term load forecasting: system implementation and performance evaluation. IEEE Transactions on Power Systems, 1999, 14(3): 1100~1105.
[88] Hippert H S, Pedreira C E, Souza R C. Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems, 2001, 16(1): 44~55.
[89]潘峰,程浩忠,杨镜非,等.基于支持向量机的电力系统短期负荷预测.电网技术,2004,38(21):39~42.
[90]李元诚,方廷健,于尔铿.短期负荷预测的支持向量机研究.中国电机工程学报,2003,23(6):55~59.
[91]赵登福,王蒙,张讲社,等.基于支撑向量机方法的短期负荷预测.中国电机工程学报,2002,22(4):26~30.
[92]杨文佳,康重庆,夏清,等.基于预测误差分布特性统计分析的概率性短期负荷预测,2006,30(19):47~52.
[93] Keydi G, Khotanzad A, Farahbakhshian N. Method for the forecasting of the probability density function of power system loads. IEEE Transations on Power Apparatus and Systems, 1981, 100(12): 5002~5010.
[94] Charytoniuk W, Chen M S, Kotas P, et al. Demand forecasting in power distribution systems using nonparametric probability density estimation. IEEE Transactions on Power Systems, 1999, 14(4): 1200~1206.
[95]赵希人,李大为.电力系统负荷预报误差的概率密度函数建模.自动化学报,1993,19(5):562~568.
[96] Ranaweera D K, Karady G G, Farmer R G. A probabilistic approach to handle uncertainties in load forecasting. in: Proceedings of the 57th Annual American Power Conference. Chicago, IL, USA, 1995.
[97]卫志农,王丹,孙国强,等.基于级联神经网络的短期负荷概率预测新方法.电工技术学报,2005,20(1):95~98.
[98]杨正瓴,王渭巍,曹东波,等.短期负荷预测的Ensemble混沌预测方法.电力系统自动化,2007,31(23):34~37.
[99] Douglas A.P., Breipohl A.M., Lee F.N., et al. Risk due to load forecast uncertainty in short term power system planning. IEEE Transactions on Power Systems, 1998, 13(4): 1493~1499.
[100] Charytoniuk W, Niebrzydowski J. Confidence interval construction for load forecast. Elcetic power systems research, 1998, 48: 97~103.
[101]邓聚龙.灰色系统理论.武汉:华中理工大学出版社,1990.
[102]齐志刚,袁晓辉,王金文.电力系统中长期负荷预测的新方法.电站系统工程,2002,18(6):39~41.
[103]何文章,宋国乡,吴爱弟.估计GM(1,1)模型中参数的一族算法.系统工程理论与实践,2005,25(1):69~75.
[104]何文章,宋国乡,吴爱弟.估计GM(1,1)模型中参数的线性规划方法.系统工程与电子技术,2004,26(2):1826~1828.
[105]郑照宁,刘德顺.基于遗传算法的改进的GM(1,1)模型IGM(1,1)直接建模.系统工程理论与实践,2003,23(5):99~102.
[106]邓新民,张明,李祚泳.基于遗传算法优化的GM(1,1)模型及效果检验.系统工程理论与实践,2002,22(8):136~139.
[107]谢开贵,李春燕,周家启.基于遗传算法的GM(1,1, )模型.系统工程学报,2000,15(2):168~172.
[108]谭冠军.GM(1,1)模型的背景值构造方法和应用(I).系统工程理论与实践,2000,20(4):98~103.
[109]张怡,魏勇,熊常伟.灰色模型GM(1,1)的一种新优化方法.系统工程理论与实践,2007,27(4):141~146.
[110]罗党,刘思峰,党耀国.灰色模型GM(1 ,1)优化.中国工程科学,2003,5(8):50~53.
[111]董奋义,田军.背景值和初始条件同时优化的GM(1,1)模型.系统工程与电子技术,2007,29(3):464~466.
[112]刘斌,赵亮,翟振杰,等.优化的GM(1,1)模型及其适用范围.南京航空航天大学学报,2003,35(4):451~454.
[113]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用(第三版).北京:科学出版社,2004.
[114] Chen C I, Chen H L, Chen S P. Forecasting of foreign exchange rates of Taiwan’smajor trading partners by novel nonlinear grey Bernoulli model NGBM(1,1). Communications in Nonlinear Science and Numerical Simulation, 2008, 13(16): 1194~1204.
[115] Morita H, Kase T, Tmura Y, et al. Interval prediction of annual maximum demand using grey dynamic model. Electrical Power and Energy System, 1996, 18(7): 409~412.
[116]庄恒扬.GM(1,1)建模机理与应用条件分析及其改进方法.系统工程理论方法应用,1993,2(3):56~62.
[117] Kennedy J, Eberhart R C. Particle swarm optimization. in: Proceedings of the IEEE International Joint Conference on Neural Networks, IEEE Press, 1995, l942~l948.
[118] Eberhart R C, Kennedy J. A New optimizer using particle swarm theory. in: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995: 39-43.
[119]曾建潮,介婧,崔志华.微粒群算法.北京:科学出版社,2004.
[120]王成山,杨军,张崇见.灰色系统理论在城市年用电量预测中的应用——不同预测方法的分析比较.电网技术,1999,23(2):15~18.
[121]王平,张亮,陈星莺.基于模糊聚类与RBF网络的短期负荷预测.继电器,2006,34(10):64~67.
[122]刘耀年,伏祥运,张文生,等.基于模糊识别与模糊聚类理论的短期负荷预测.电工技术学报,2002,17(5):83~86, 95.
[123]孟丹.基于区间值的模糊聚类分析.辽宁师范大学学报(自然科学版),2003,26(2):113~116.
[124] Ho J K, Tae-Wan R. Time series prediction using an interval arithmetic FIR network.Neuron Information Processing-Letters and Reviews, 2005, 8(3): 39~47.
[125] Hemandez C A, Espf J, Nakayama K, et al. Interval Arithmetic Backpropagation. in: Proceedings of 1993 International Joint Conference on Neural Networks. 1993, 375~378.
[126] Sun Ke, Kang Chongqing, Xu Ruilin, et al. Analyzing the impact of weather factorson daily electrical demand. in: Proceedings of the 5th International Conference on Power Transmission and Distribution Technology. Beijing, China, 2005, 310~316,.
[127]高新波.模糊聚类分析及其应用.西安:西安电子科技大学出版社,2004.
[128] Alefeld G, Herzberger J. Introduction to Interval Computations. New York: Academic Press, 1983.
[129]杨正瓴,林孔元,余贻鑫.短期负荷预报的“双周期加混沌”法中的子模型优选理论探讨.电网技术,2003,27(5):34~36.
[130] O’N eill-Carrillo E, Heydt G T, Kostelich E J. Chaotic phenomena in power systems:detection and applications. Electric Machines and Power Systems, 1999, (27):79~91.
[131]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉大学出版社,2002.
[132]韩敏.混沌时间序列预测理论与方法.北京:中国水利水电出版社,2007.
[133]杨正瓴,林孔元.短期负荷可预报天数的初步研究.电力系统自动化,2002,26(11):14~18.
[134]杨正瓴,田勇,林孔元.短期负荷预测的“双周期加混沌”法中的多步法与气象因子的使用.电网技术,2004,28(12):20~24.
[135] Sugihara G, May R. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 1990, 344(6268): 734~741.
[136]侯媛彬,汪梅,王立琦.系统辨识及其MATLAB仿真.北京:科学出版社,2004.
[137] Cai M L, Cai F, Shi A G, et al. Chaotic time series prediction based on local-region multi-steps forecasting model. Lecture Notes in Computer Science, 2004, 3174: 418~423.
[138] Packard N H, Crutcheld J P, Farmer J D, et al. Geometry from a time series. Physical Review Letters, 1980, 45(9): 712~716.
[139]雷绍兰,孙才新,周湶，等．电力短期负荷的多嵌入维一阶局域预测．电网技术,2005,29(13):45~49.
[140]杨正瓴,林孔元.短期负荷预测相空间重构参数优选的数值分析与测试.电力系统自动化,2002,27(16):40~44.
[141]谷子,唐巍.电力短期负荷时间序列混沌相空间重构参数优选法.中国电机工程学报,2006,26(14):18~23.
[142]毛国君,段立娟,王实,等.数据挖掘原理与算法.北京:清华大学出版社,2005.
[143]蒋传文,袁智强,侯志俭,等.高嵌入维混沌负荷序列预测方法研究.电网技术,2004,28(3):25~28.
[144]叶中行,龙如军.混沌时间序列的区间预测.上海交通大学学报,1997,31(2):7~12..
[145]王健,文福拴,杨仁刚.基于发电容量充裕度估计的发电公司检修策略.电力系统自动化,2005,29(6):45~50.
[146] DiCiccio T J, Efron B. Bootstrap confidence intervals. Statistic. Science, 1996, 11(3): 189–228.
[147] Bordignon S, Lisi F. Predictive accuracy for chaotic economic models. Economics Letters, 2001, 70: 51~58.
[148]茆诗松,王静龙,濮晓龙.高等数理统计.北京:高等教育出版社,施普林格出版社,1998.
[2]康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨.电力系统自动化,2004,28(17):1~11.
[3]张峰.电力负荷管理技术.北京:中国电力出版社,2005.
[4]刘晨晖.电力系统负荷预报理论及方法.哈尔滨:哈尔滨工业大学出版社,1987.
[5]牛东晓,曹树华,赵磊,等.电力负荷预测技术及其应用.北京:中国电力出版社,1998.
[6] Ranaweera D K, Karady G G, Farmer R G. Economic impact analysis of load forecasting. IEEE Transactions on Power Systems, 1997, 12(3): 1388~1392.
[7] Douglas A P, Breipohl A M, Lee F N, et al. Risk due to load forecast uncertainty in short term power system planning. IEEE Transactions on Power Systems, 1998, 13(4): 1493~1499.
[8]赵希正.中国电力负荷特性分析与预测.北京:中国电力出版社,2002.
[9]肖国泉,王春,张福伟.电力负荷预测.北京:中国电力出版社,2001.
[10]张伏生,刘芳,赵文斌,等.灰色Verhulst模型在中长期负荷预测中的应用.电网技术,2003,27(5):37~39,81.
[11]曹国剑,黄纯,隆辉,等.基于GM(1,1)改进模型的电网负荷预测.电网技术,2004,28(13):50~53.
[12]余健明,燕飞,杨文宇,等.中长期电力负荷的变权灰色组合预测模型.电网技术,2005,29(17):26~29.
[13]张大海,史开泉,江世芳.灰色负荷预测的参数修正法.电力系统及其自动化学报,2001,4(2):20~22.
[14]朱芸,乐秀璠.可变参数无偏灰色模型的中长期负荷预测.电力自动化设备,2003,23(4):25~27.
[15]顾洁,申刚,徐光虎.改进的电力系统中长期负荷预测方法研究.电力自动化设备,2002,22(6):1~4.
[16]王吉权,赵玉林.生长曲线在电力负荷预测中的应用.电网技术,2004,28(22):36~39.
[17]宁波,康重庆,夏清.中长期负荷预测模型的扩展策略.中国电力,2000,33(10):36~38.
[18]刘遵敬.最小概率最大化回归方法在电力负荷中期预测中的应用.中国电力,2004,37(9):50~54.
[19]陈柔伊,张尧,武志刚,等.改进的模糊聚类方法在负荷预测中的应用.电力系统及其自动化学报,2005,17(3):73~77.
[20]伍力,吴捷,叶军.负荷中长期预测中一种改进的模糊聚类算法.电网技术,2000,24(1):36~38.
[21]顾洁.电力系统中长期负荷预测的模糊算法.上海交通大学学报,2002,36(2):255~258.
[22]杨宗麟,吴德伟.利用“计量经济模型”预测电力负荷.华东电力,1997(3):1~5.
[23]韦凌云,吴捷,刘永强.基于系统动力学的电力系统中长期负荷预测.电力系统自动化,2000,24(16):44~47.
[24]钟庆,吴捷,伍力,等.基于系统动力学的分区负荷预测.电网技术,2001,25(3):51~55.
[25]谢敬东,唐国庆,徐高飞,等.组合预测方法在电力负荷预测中的应用.中国电力,1998,31(6):3~5.
[26]康重庆,夏清,沈渝,等.电力系统负荷预测的综合模型.清华大学学报,1999,39(1):8~11.
[27]余健明,燕飞,杨文宇,等.中长期电力负荷的变权灰色组合预测模型.电网技术,2005,29(17):26~29.
[28] Chen G. J, Li K K, Chung T S, et al. Application of an innovative combined forecasting method in power system load forecasting, Electric Power Systems Research, 2001, 59: 131~137.
[29]赵海清,牛东晓.负荷预测的交叉式自适应优选组合预测模型.华北电力大学学报,2000,27(4):13~17.
[30]高峰,康重庆,夏清,等.负荷预测中多模型的自动筛选方法.电力系统自动化,2004,28(6):11~13,40.
[31] Charytoniuk W, Chen M S, Kotas P, et al. Demand forecasting in power distribution systems using nonparametric probability density estimation. IEEE Transactions on Power Systems, 1999, 14(4):1200~1206.
[32]刘健,徐精求,董海鹏.配电网概率负荷分析及其应用.电网技术,2004,28(6):67~75.
[33]康重庆,杨高峰,夏清.电力需求的不确定性分析.电力系统自动化,2005,29(17):14~19.
[34]杨高峰,康重庆,徐国新,等.多维序列运算理论及其在不确定性电力需求分析中的应用.中国电机工程学报,2005,25(25):12~17.
[35]康重庆.序列运算理论及其应用.北京:清华大学出版社,2003.
[36] Alfares H K, Nazeeruddin M. Electric load forecasting: literature survey and classification of methods. International Journal of Systems Science, 2002, 33(1): 23~34.
[37]尚金成,黄永晧,夏清,等.电力市场理论研究与应用.北京:中国电力出版社,2002.
[38] Papalexopoulos A D, Hesterberg T C. A regression-based approach to short-term system load forecasting, IEEE Transactions on Power Systems 1990, 5(4): 1535~1547.
[39]王士政.电网调度自动化与配网自动化技术(第二版).北京:中国水利水电出版社,2006.
[40]田忠林.短期负荷预测自回归动平均模型的辨识方法.东北电力技术,1999,11:6~9.
[41]叶玫昀,罗耀华,刘勇,等.基于ARMA模型的电力系统短期负荷预测方法研究.信息技术,2002,6:74~76.
[42] Shy-Jier Huang, Kuang-Rong Shih. Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Transactions on Power Systems, 2003, 18(2): 205~215.
[43] N. Amjady, Short-term hourly load forecasting using time-series modeling with peak load estimation capability, IEEE Transactions on Power Systems, 2001, 16(3): 498~505.
[44] Chen J F, Wang W M, Huang C M. Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting, Electric Power Systems Research, 1995, 34: 187~196.
[45]顾洁.应用小波分析进行短期负荷预测.电力系统及其自动化学报,2003,15(2):40~65.
[46] Tai N, Jurgen S, Wu H X. Techniques of applying wavelet transform into combined model for short-term load forecasting. Electric Power Systems Research, 2006, 76(6-7): 525~533.
[47]王家红,黄阿强,熊信艮.基于小波网络的短期负荷预测方法.电力自动化设备,2003,23(3):11~12.
[48]李天云,刘自发.电力系统负荷的混沌特性及预测.中国电机工程学报,2000,20(11):36~40.
[49]吕金虎,占勇,陆君安.电力系统短期负荷预测的非线性混沌改进模型.中国电机工程学报,2000,20(12):80~83.
[50]蒋传文,权先璋,李承军,等.混沌理论在电力系统负荷预测中的应用.武汉交通科技大学学报,1999,23(6):608~611.
[51]温权,张勇传,程时杰.负荷预报的混沌时间序列分析法.电网技术,2001,25(10):13~16.
[52]梁志珊,王丽敏,付大鹏.应用混沌理论的电力系统短期负荷预测.控制与决策,1998,13(1):87~94.
[53]雷绍兰,孙才新,刘凡,等.电力短期负荷的混沌局域关联性预测.重庆大学学报(自然科学版),2005,28(5):24~28.
[54] Choi J G, Park J K, Kim K H, et al. Daily peak load forecasting system using a chaotic time series.in: Proceedings of the International Conference on Intelligent Systems Applications to Power Systems. Orlando, USA, 1996, 283~287.
[55] Arango H G, de Souza A C Z, Lambert-Torres G, et al. Difference between regular and deterministic chaos processes based on time analysis of load: an example using CEMIG data. Electric Power Systems Research, 2000, 56(1): 35~41.
[56] Mori H, Urano S. Short-term load forecasting with chaos time series analysis. in: Proceedings of the International Conference on Intelligent Systems Applications to Power Systems (SAP’96). 1996, 133~137.
[57]许涛,贺仁睦,王鹏,等.基于输入空间压缩的短期负荷预测.电力系统自动化,2004,28(6):51~56.
[58] Ruzic S, Vuckovic A, Nikolic N. Weather sensitive method for short-term load forecasting in electric power utility of Serbia. IEEE Transactions on Power Systems, 2003, 18(4): 1581~1586.
[59]汪峰,于尔铿,阎承山,等.基于因素影响的电力系统短期负荷预报方法的研究.中国电机工程学报,1999,19(8):54~58.
[60]张国江,邱家驹,李继红.基于模糊推理系统的多因素电力负荷预测.电力系统自动化,2002,26(5):49~53.
[61]康重庆,程旭,夏清,等.一种规范化的处理相关因素的短期负荷预测策略.电力系统自动化,1999,23(18):32~35.
[62]胡阳.考虑气象因素短期负荷预测的理论与算法的研究.北京:清华大学,1998.
[63] Park D C, EI-Sharkawi M A, Marks R J. Electric load forecasting using artificial neural network. IEEE Transactions on Power Systems, 1991, 6(2): 442~449.
[64] Lee K Y, Cha Y T, Park J H. Short term load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 1992, 7(1): 124~130.
[65] Mori H, Yuibara A. Deterministic annealing clustering for ANN-based short-term load forecasting. IEEE Transactions on Power Systems, 2001, 16(3): 545~551.
[66] Chang-il K, In-keun Y, Song Y H. Kohonen neural network and wavelet transform based approach to short-term load forecasting. Electric Power Systems Research, 2002, 63(2): 169~176.
[67] Alsayegh O A. Short-term load forecasting using seasonal artificial neural networks. International Journal of Power and Engery Systems, 2003, 23(3): 137~142.
[68] Kiartzis S J, Bakirtzis A G., Petridis V. Short-term load forecasting using neural networks. Electric Power Systems Research, 1995, 33:1~6.
[69] Ranaweera D K, Hubele N F, Paplexopoulos A D. Application of radial basis function neural network model for short-term load forecasting. IEE Proceedings- Generation Transmission and Distribution, 1995, 142(1): 45~50.
[70]高山,单渊达.基于径向基函数网络的短期负荷预测.电力系统自动化,1999,23(5):31~34.
[71]彭建春,江荣汉.电力系统短期负荷预报的动态神经网络方法.湖南大学学报,1997,24(1):83~86.
[72] AlFuhaid A S, El-Sayed M A, Mahmoud M S, Cascaded artificial neural networks for short-term load forecasting, IEEE Transactions on Power Systems, 1997, 12(4):1524~1529.
[73] Chen S T, Yu D C, Moghaddamjo A R. Weather sensitive short-term load forecasting using nonfully connected artificial neural network. IEEE Transactions on Power Systems, 1992, 7(3): 1098~1105.
[74] Ho K L, Hsu Y-Y, Yang C-C. Short-term load forecasting using a multilayer neural network with an adaptive learning algorithm. IEEE Transactions on Power systems, 1992, 7(1):141~149.
[75]丁军威,孙雅明.基于混沌学习算法的神经网络短期负荷预测.电力系统自动化,2000,24(2):32~35.
[76]王民量,张伯明,夏清.电力系统短期负荷预测的共轭梯度ANN方法.电力系统自动化,1999,23(1):34~36.
[77] Srinivasan D. Evolving artificial neural networks for short term load forecasting. Neurocomputing, 1998, 23(1): 265~276.
[78] Bakirtzis A G, Theocharis J B, Kiartzis S J, et al. Short-term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 1995, 10(3):1518~1524.
[79]牛东晓,邢棉,谢宏,等.短期电力负荷预测的小波神经网络模型的研究.电网技术,1999,23(4):21~24.
[80]孙雅明,张智晟.相空间重构和混沌神经网络融合的短期负荷预测研究.中国电机工程学报,2004,24(1):44~48.
[81]岑文辉,雷友坤,谢恒.应用人工神经网络与遗传算法进行短期负荷预测.电力系统自动化,1997,21(3):29~32.
[82]陈耀武,汪乐宇,龙洪玉.基于组合式神经网络的短期电力负荷预测模型.中国电机工程学报,2001,21(4):79~82.
[83]甘文泉,胡保生.用自适应神经元网络进行短期电力负荷预测.电网技术,1997,21(3):28~31.
[84]贺蓉,曾刚,姚建刚,等.天气敏感性神经网络在地区电网短期负荷预测中的应用.电力系统自动化,2001,25(17):32~35,52.
[85]姚李孝,宋玲芳,李庆宇等.基于模糊聚类分析与BP网络的电力系统短期负荷预测.电网技术,2005,29(1):20~23.
[86]姜勇.基于模糊聚类的神经网络短期负荷预测方法.电网技术,2003,27(2):45~49.
[87] Srinivasan D, Swee S T, Chang C S, et al. Parallel neural network-fuzzy Expert system strategy for short-term load forecasting: system implementation and performance evaluation. IEEE Transactions on Power Systems, 1999, 14(3): 1100~1105.
[88] Hippert H S, Pedreira C E, Souza R C. Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems, 2001, 16(1): 44~55.
[89]潘峰,程浩忠,杨镜非,等.基于支持向量机的电力系统短期负荷预测.电网技术,2004,38(21):39~42.
[90]李元诚,方廷健,于尔铿.短期负荷预测的支持向量机研究.中国电机工程学报,2003,23(6):55~59.
[91]赵登福,王蒙,张讲社,等.基于支撑向量机方法的短期负荷预测.中国电机工程学报,2002,22(4):26~30.
[92]杨文佳,康重庆,夏清,等.基于预测误差分布特性统计分析的概率性短期负荷预测,2006,30(19):47~52.
[93] Keydi G, Khotanzad A, Farahbakhshian N. Method for the forecasting of the probability density function of power system loads. IEEE Transations on Power Apparatus and Systems, 1981, 100(12): 5002~5010.
[94] Charytoniuk W, Chen M S, Kotas P, et al. Demand forecasting in power distribution systems using nonparametric probability density estimation. IEEE Transactions on Power Systems, 1999, 14(4): 1200~1206.
[95]赵希人,李大为.电力系统负荷预报误差的概率密度函数建模.自动化学报,1993,19(5):562~568.
[96] Ranaweera D K, Karady G G, Farmer R G. A probabilistic approach to handle uncertainties in load forecasting. in: Proceedings of the 57th Annual American Power Conference. Chicago, IL, USA, 1995.
[97]卫志农,王丹,孙国强,等.基于级联神经网络的短期负荷概率预测新方法.电工技术学报,2005,20(1):95~98.
[98]杨正瓴,王渭巍,曹东波,等.短期负荷预测的Ensemble混沌预测方法.电力系统自动化,2007,31(23):34~37.
[99] Douglas A.P., Breipohl A.M., Lee F.N., et al. Risk due to load forecast uncertainty in short term power system planning. IEEE Transactions on Power Systems, 1998, 13(4): 1493~1499.
[100] Charytoniuk W, Niebrzydowski J. Confidence interval construction for load forecast. Elcetic power systems research, 1998, 48: 97~103.
[101]邓聚龙.灰色系统理论.武汉:华中理工大学出版社,1990.
[102]齐志刚,袁晓辉,王金文.电力系统中长期负荷预测的新方法.电站系统工程,2002,18(6):39~41.
[103]何文章,宋国乡,吴爱弟.估计GM(1,1)模型中参数的一族算法.系统工程理论与实践,2005,25(1):69~75.
[104]何文章,宋国乡,吴爱弟.估计GM(1,1)模型中参数的线性规划方法.系统工程与电子技术,2004,26(2):1826~1828.
[105]郑照宁,刘德顺.基于遗传算法的改进的GM(1,1)模型IGM(1,1)直接建模.系统工程理论与实践,2003,23(5):99~102.
[106]邓新民,张明,李祚泳.基于遗传算法优化的GM(1,1)模型及效果检验.系统工程理论与实践,2002,22(8):136~139.
[107]谢开贵,李春燕,周家启.基于遗传算法的GM(1,1, )模型.系统工程学报,2000,15(2):168~172.
[108]谭冠军.GM(1,1)模型的背景值构造方法和应用(I).系统工程理论与实践,2000,20(4):98~103.
[109]张怡,魏勇,熊常伟.灰色模型GM(1,1)的一种新优化方法.系统工程理论与实践,2007,27(4):141~146.
[110]罗党,刘思峰,党耀国.灰色模型GM(1 ,1)优化.中国工程科学,2003,5(8):50~53.
[111]董奋义,田军.背景值和初始条件同时优化的GM(1,1)模型.系统工程与电子技术,2007,29(3):464~466.
[112]刘斌,赵亮,翟振杰,等.优化的GM(1,1)模型及其适用范围.南京航空航天大学学报,2003,35(4):451~454.
[113]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用(第三版).北京:科学出版社,2004.
[114] Chen C I, Chen H L, Chen S P. Forecasting of foreign exchange rates of Taiwan’smajor trading partners by novel nonlinear grey Bernoulli model NGBM(1,1). Communications in Nonlinear Science and Numerical Simulation, 2008, 13(16): 1194~1204.
[115] Morita H, Kase T, Tmura Y, et al. Interval prediction of annual maximum demand using grey dynamic model. Electrical Power and Energy System, 1996, 18(7): 409~412.
[116]庄恒扬.GM(1,1)建模机理与应用条件分析及其改进方法.系统工程理论方法应用,1993,2(3):56~62.
[117] Kennedy J, Eberhart R C. Particle swarm optimization. in: Proceedings of the IEEE International Joint Conference on Neural Networks, IEEE Press, 1995, l942~l948.
[118] Eberhart R C, Kennedy J. A New optimizer using particle swarm theory. in: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995: 39-43.
[119]曾建潮,介婧,崔志华.微粒群算法.北京:科学出版社,2004.
[120]王成山,杨军,张崇见.灰色系统理论在城市年用电量预测中的应用——不同预测方法的分析比较.电网技术,1999,23(2):15~18.
[121]王平,张亮,陈星莺.基于模糊聚类与RBF网络的短期负荷预测.继电器,2006,34(10):64~67.
[122]刘耀年,伏祥运,张文生,等.基于模糊识别与模糊聚类理论的短期负荷预测.电工技术学报,2002,17(5):83~86, 95.
[123]孟丹.基于区间值的模糊聚类分析.辽宁师范大学学报(自然科学版),2003,26(2):113~116.
[124] Ho J K, Tae-Wan R. Time series prediction using an interval arithmetic FIR network.Neuron Information Processing-Letters and Reviews, 2005, 8(3): 39~47.
[125] Hemandez C A, Espf J, Nakayama K, et al. Interval Arithmetic Backpropagation. in: Proceedings of 1993 International Joint Conference on Neural Networks. 1993, 375~378.
[126] Sun Ke, Kang Chongqing, Xu Ruilin, et al. Analyzing the impact of weather factorson daily electrical demand. in: Proceedings of the 5th International Conference on Power Transmission and Distribution Technology. Beijing, China, 2005, 310~316,.
[127]高新波.模糊聚类分析及其应用.西安:西安电子科技大学出版社,2004.
[128] Alefeld G, Herzberger J. Introduction to Interval Computations. New York: Academic Press, 1983.
[129]杨正瓴,林孔元,余贻鑫.短期负荷预报的“双周期加混沌”法中的子模型优选理论探讨.电网技术,2003,27(5):34~36.
[130] O’N eill-Carrillo E, Heydt G T, Kostelich E J. Chaotic phenomena in power systems:detection and applications. Electric Machines and Power Systems, 1999, (27):79~91.
[131]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉大学出版社,2002.
[132]韩敏.混沌时间序列预测理论与方法.北京:中国水利水电出版社,2007.
[133]杨正瓴,林孔元.短期负荷可预报天数的初步研究.电力系统自动化,2002,26(11):14~18.
[134]杨正瓴,田勇,林孔元.短期负荷预测的“双周期加混沌”法中的多步法与气象因子的使用.电网技术,2004,28(12):20~24.
[135] Sugihara G, May R. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 1990, 344(6268): 734~741.
[136]侯媛彬,汪梅,王立琦.系统辨识及其MATLAB仿真.北京:科学出版社,2004.
[137] Cai M L, Cai F, Shi A G, et al. Chaotic time series prediction based on local-region multi-steps forecasting model. Lecture Notes in Computer Science, 2004, 3174: 418~423.
[138] Packard N H, Crutcheld J P, Farmer J D, et al. Geometry from a time series. Physical Review Letters, 1980, 45(9): 712~716.
[139]雷绍兰,孙才新,周湶，等．电力短期负荷的多嵌入维一阶局域预测．电网技术,2005,29(13):45~49.
[140]杨正瓴,林孔元.短期负荷预测相空间重构参数优选的数值分析与测试.电力系统自动化,2002,27(16):40~44.
[141]谷子,唐巍.电力短期负荷时间序列混沌相空间重构参数优选法.中国电机工程学报,2006,26(14):18~23.
[142]毛国君,段立娟,王实,等.数据挖掘原理与算法.北京:清华大学出版社,2005.
[143]蒋传文,袁智强,侯志俭,等.高嵌入维混沌负荷序列预测方法研究.电网技术,2004,28(3):25~28.
[144]叶中行,龙如军.混沌时间序列的区间预测.上海交通大学学报,1997,31(2):7~12..
[145]王健,文福拴,杨仁刚.基于发电容量充裕度估计的发电公司检修策略.电力系统自动化,2005,29(6):45~50.
[146] DiCiccio T J, Efron B. Bootstrap confidence intervals. Statistic. Science, 1996, 11(3): 189–228.
[147] Bordignon S, Lisi F. Predictive accuracy for chaotic economic models. Economics Letters, 2001, 70: 51~58.
[148]茆诗松,王静龙,濮晓龙.高等数理统计.北京:高等教育出版社,施普林格出版社,1998.
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