基于EMD方法的电力系统短期负荷预测
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摘要
电力系统短期负荷预测关系到电力系统的调度运行和生产计划,准确的负荷预测有助于提高系统的安全性和稳定性,能够减少发电成本。随着电力市场的建立和发展,短期负荷预测正在发挥越来越重要的作用。
短期负荷变化规律复杂,并受到多方面因素的影响,分析预测时将各个不同的负荷成分从总负荷中分别提取出来单独进行研究,将有利于提高预测精度。本文在对经验模态分解法(EMD分解,Empirical Mode Decomposition)进行研究的基础上,将该理论引入电力负荷预测领域,提出了一种基于EMD分解的短期负荷预测方法。
本文首先介绍了经验模态分解理论的发展历程和它的主要应用领域,详细描述了EMD算法中的基本概念和分解原理,根据电力系统负荷的组成和特点,提出了建立基于经验模态分解的短期负荷预测模型,该预测模型在将EMD理论与ARMA相结合,利用EMD算法对负荷序列进行分解,分解所得到的每一个基本模式分量(IMF分量)分别进行ARMA预测。
其次,为了解决短期负荷预测中气象因素对预测结果的影响,本文对电力系统短期负荷预测中如何考虑气温因素的因素进行了探讨,提出了将干预分析和EMD分解相结合的短期负荷预测模型,应用干预分析模型将气温影响负荷从原始负荷中进行剔除后再对净化的负荷序列进行研究。
然后,本文以某地区2008年4月至9月的负荷作为算例,对该时期内的负荷序列进行了基于经验模态分解的负荷预测。利用EMD分解,将负荷分量分解为若干分量,对得到的各个分量特点并进行归类,利用ARMA模型预测了每一个分量在未来一段时间内的数值,将各分量预测结果相加,得到负荷在该时段内的总预测值。仿真结果表明,与普通的时间序列预测法相比,采用经验模态分解进行短期负荷预测,对于预测的精度有着明显的改善和提高。
最后,本文利用预测干预分析模型对负荷序列进行处理,从原始负荷序列中剥离出气温影响负荷,并对净化后的负荷序列进行如上的EMD分解和每一个IMF的ARMA模型预测,进而得到净化负荷在该时段内的总预测值,然后将此预测结果与同时期内未经干预分析模型而进行直接进行EMD分解和ARMA模型预测得到的结果进行比较,仿真和计算的结果表明,采用干预预测模型后的短期负荷预测对于预测的精度有着进一步的改善和提高。
算例结果验证了本文所提出的方法和预测模型,能够有效地提高短期负荷预测的精度,是电力系统短期负荷预测领域一项有益的尝试和探讨。
Short term load forecasting has been attracting much attention of people. It is very important in the power sector. Short-term load forecasting is related to the power system operation and production scheduling, an accurate load forecasting will help to improve power system security and stability, and to reduce the cost of electricity. With the establishment and development of the electricity market, short-term load forecasting is playing an increasingly important role.
The rule of changes of short-term load which is not regularity is based on the load data and affected by many factors. Because of the difference of the load components, it is useful to improve the forecast accuracy for research if we extract the separate load components from the total load when we analyze and predict the load. The research in this article is based on Empirical Mode Decomposition, EMD will be used in the power load forecasting, a forecasting method based on EMD decomposition is proposed.
Firstly, the methods of short-term load forecasting being used currently are summarized in this paper and the advantages and disadvantages of each method are discussed, the composition and characteristics of power system load have been discussed in the article, and the establishment of empirical mode decomposition model has also been proposed, this model combine EMD and ARMA, using the ARMA to predict the each of IMF components.
Secondly, in order to address short-term load forecasting of meteorological factors on the results of prediction, this paper discusses the factors to consider the temperature of power system load forecasting, use intervention analysis model to remove the temperature of the load from the original load .
Then, a district in 2008 spring and summer electricity load segment as an example, by using EMD decomposition, the load component is divided into several components. In the analysis of the characteristics of each component and are classified, the use of ARMA models to predict the future of each component in the value over time, add these forecasts, and then load in that period by the total forecast. This simulation results show that EMD for short-term load forecasting, the prediction accuracy has significantly improved and enhanced.
At last, this article take weather factor as a interference then use a established intervention analysis model to strip out the temperature of the load from the original load, take the purified load sequence to repeat the processing of EMD decomposition and the ARMA model of each IMF Prediction, then get the forecasting value of the purified load, compare the results of this prediction with the same period without the intervention, simulation and calculation results show that the accuracy of load forecast which treated by the intervention of short-term forecasting model has further improved and enhanced.
The simulation results strongly validate the proposed method and prediction models, it can effectively improve the accuracy of short-term load forecasting, power system it is a useful short-term load forecasting attempts and discussion.
短期负荷变化规律复杂,并受到多方面因素的影响,分析预测时将各个不同的负荷成分从总负荷中分别提取出来单独进行研究,将有利于提高预测精度。本文在对经验模态分解法(EMD分解,Empirical Mode Decomposition)进行研究的基础上,将该理论引入电力负荷预测领域,提出了一种基于EMD分解的短期负荷预测方法。
本文首先介绍了经验模态分解理论的发展历程和它的主要应用领域,详细描述了EMD算法中的基本概念和分解原理,根据电力系统负荷的组成和特点,提出了建立基于经验模态分解的短期负荷预测模型,该预测模型在将EMD理论与ARMA相结合,利用EMD算法对负荷序列进行分解,分解所得到的每一个基本模式分量(IMF分量)分别进行ARMA预测。
其次,为了解决短期负荷预测中气象因素对预测结果的影响,本文对电力系统短期负荷预测中如何考虑气温因素的因素进行了探讨,提出了将干预分析和EMD分解相结合的短期负荷预测模型,应用干预分析模型将气温影响负荷从原始负荷中进行剔除后再对净化的负荷序列进行研究。
然后,本文以某地区2008年4月至9月的负荷作为算例,对该时期内的负荷序列进行了基于经验模态分解的负荷预测。利用EMD分解,将负荷分量分解为若干分量,对得到的各个分量特点并进行归类,利用ARMA模型预测了每一个分量在未来一段时间内的数值,将各分量预测结果相加,得到负荷在该时段内的总预测值。仿真结果表明,与普通的时间序列预测法相比,采用经验模态分解进行短期负荷预测,对于预测的精度有着明显的改善和提高。
最后,本文利用预测干预分析模型对负荷序列进行处理,从原始负荷序列中剥离出气温影响负荷,并对净化后的负荷序列进行如上的EMD分解和每一个IMF的ARMA模型预测,进而得到净化负荷在该时段内的总预测值,然后将此预测结果与同时期内未经干预分析模型而进行直接进行EMD分解和ARMA模型预测得到的结果进行比较,仿真和计算的结果表明,采用干预预测模型后的短期负荷预测对于预测的精度有着进一步的改善和提高。
算例结果验证了本文所提出的方法和预测模型,能够有效地提高短期负荷预测的精度,是电力系统短期负荷预测领域一项有益的尝试和探讨。
Short term load forecasting has been attracting much attention of people. It is very important in the power sector. Short-term load forecasting is related to the power system operation and production scheduling, an accurate load forecasting will help to improve power system security and stability, and to reduce the cost of electricity. With the establishment and development of the electricity market, short-term load forecasting is playing an increasingly important role.
The rule of changes of short-term load which is not regularity is based on the load data and affected by many factors. Because of the difference of the load components, it is useful to improve the forecast accuracy for research if we extract the separate load components from the total load when we analyze and predict the load. The research in this article is based on Empirical Mode Decomposition, EMD will be used in the power load forecasting, a forecasting method based on EMD decomposition is proposed.
Firstly, the methods of short-term load forecasting being used currently are summarized in this paper and the advantages and disadvantages of each method are discussed, the composition and characteristics of power system load have been discussed in the article, and the establishment of empirical mode decomposition model has also been proposed, this model combine EMD and ARMA, using the ARMA to predict the each of IMF components.
Secondly, in order to address short-term load forecasting of meteorological factors on the results of prediction, this paper discusses the factors to consider the temperature of power system load forecasting, use intervention analysis model to remove the temperature of the load from the original load .
Then, a district in 2008 spring and summer electricity load segment as an example, by using EMD decomposition, the load component is divided into several components. In the analysis of the characteristics of each component and are classified, the use of ARMA models to predict the future of each component in the value over time, add these forecasts, and then load in that period by the total forecast. This simulation results show that EMD for short-term load forecasting, the prediction accuracy has significantly improved and enhanced.
At last, this article take weather factor as a interference then use a established intervention analysis model to strip out the temperature of the load from the original load, take the purified load sequence to repeat the processing of EMD decomposition and the ARMA model of each IMF Prediction, then get the forecasting value of the purified load, compare the results of this prediction with the same period without the intervention, simulation and calculation results show that the accuracy of load forecast which treated by the intervention of short-term forecasting model has further improved and enhanced.
The simulation results strongly validate the proposed method and prediction models, it can effectively improve the accuracy of short-term load forecasting, power system it is a useful short-term load forecasting attempts and discussion.
引文
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[2]于尔铿,刘广一,周京阳.能量管理系统(EMS).科学出版社,1998
[3]刘晨晖著.电力系统负荷预报理论及方法[M].哈尔滨:哈尔滨工业大学出版社,1987
[4]牛东晓,曹树华,赵磊等编著.电力负荷预测技术及其应用[M].北京:中国电力出版社,1998
[5]康重庆,夏清,刘梅,相年德.应用于负荷预测中的回归分析的特殊问题[J].电力系统自动化,1998,22(10):38-41
[6]赵宏伟,任震,黄雯莹.基于周期自回归模型的短期负荷预测[J].中国电机工程学报,1997,17(5):347-350
[7]谢洁树,电力负荷预测的方法研究[J].绝缘材料, 2008,04(20) :67-70
[8]李颖峰.改进灰色模型在电力负荷预测中的应用[J].电网与清洁能源,2009,25(3):10-11
[9]姜竹楠,基于小波包负荷特征提取和径向基网络的短期负荷预测新方法[J].电力科学与技术学报, 2007,21(02):34-38
[10]李云天,刘自发,电力系统负荷的混沌特性及预测[J].中国电机工程学报, 2000,20(11):36-40
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