基于最小二乘支持向量机的短期电力负荷预测方法研究
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摘要
负荷预测是电力系统调度和计划部门的主要工作之一,准确的负荷预测对于保证电力系统安全、稳定和经济运行以及在电力市场环境下提高供电部门的经济效益和社会效益都具有重要而深远的意义。本文阐述了负荷预测的相关基础理论,对各种短期负荷预测方法以及研究情况进行了归纳、总结。介绍了统计学习理论的一些重要概念、主要思想以及在该理论框架下发展起来的新的通用学习算法——支持向量机,并利用标准支持向量机的一种扩展、变形算法——最小二乘支持向量机建立了短期负荷预测模型。
最小二乘支持向量机的模型参数对模型的学习能力具有重要影响,但目前尚未形成统一有效的选择方法。针对该问题,本文根据短期电力负荷在相关影响因素相近时其负荷变化规律也相似的特点,提出了一种联合相似日和蚁群算法的模型参数选择方法,即根据不同影响因素对负荷预测影响的程度不同的特点,采用改进关联度公式选取若干历史相似日作为训练样本和测试样本,并引进新型的全局搜索算法——蚁群算法来优化选择参数。实例分析表明该方法所选择的参数更具合理性,提高了模型的泛化能力。
介绍了一种基于概率统计的异常数据辨识方法,对辨识出的异常数据分别进行纵向、横向修正。结合某地方电网的实际负荷数据和气象数据,分析了短期负荷预测的影响因素,特别是气象条件以及气象条件的累积效应对短期负荷预测的影响。通过采用不同气象条件的“累积阈值函数”进行数值折算,从而可以根据气象数值的变化对这种累积效应进行定量描述。
在综合考虑相关影响因素的基础上,利用上述参数选择方法所确定的最小二乘支持向量机模型进行实例预测,验证了本文所建预测模型的有效性。
Load forecasting is one of the key tasks for scheduling and planning departments of power system and the accurate load forecasting is very important to guarantee the power system security, stability and economic operation, as well as increase the electricity sector under the economic and social benefits in the electricity market environment. In this paper, the relevant basis theories of load forecasting are introduced and existing short-term load forecasting methods are summed up. some important concepts and the main idea of statistical learning theory(SLT) and a new generic learning algorithm - support vector machine(SVM) in the theoretical framework of statistical learning theory are introduced, and a short-term load forecasting model is established based on least squares support vector machine(LS-SVM), which is an expansion of the standard SVM.
LS-SVM's model parameters have an important impact on learning ability, which is not yet determined by an unified and effective choice method. In this paper, a choice method of model parameters based on similar days and ant colony algorithm is proposed, that is, according to the features of different effect for load with different effect factors, some similar days are separated into training samples and test samples with an improved grey relational degree, and then a new global search algorithm -ant colony algorithm is implemented to optimize parameters. The practical example shows that it's more reasonable and enhances the generalization ability of the model using the method to choose parameters.
An identification method of abnormal datas based on statistical probability is introduced, and vertical and horizontal amendments are also given for the abnormal datas. An analysis is given on the effect factors of short-term load forecasting, and in particular weather conditions and the accumulated effects of weather conditions with the actual load datas and weather datas of a local power network. By using "accumulated threshold function" of different weather conditions to quantify the effect, the accumulated effects of weather conditions can be described with the changes of numerical value.
On the basis of related effect factors, a practical example is given using the LS-SVM's model which is constructed by the former choice method of model parameters, and the results show the effectiveness of the model in this paper.
最小二乘支持向量机的模型参数对模型的学习能力具有重要影响,但目前尚未形成统一有效的选择方法。针对该问题,本文根据短期电力负荷在相关影响因素相近时其负荷变化规律也相似的特点,提出了一种联合相似日和蚁群算法的模型参数选择方法,即根据不同影响因素对负荷预测影响的程度不同的特点,采用改进关联度公式选取若干历史相似日作为训练样本和测试样本,并引进新型的全局搜索算法——蚁群算法来优化选择参数。实例分析表明该方法所选择的参数更具合理性,提高了模型的泛化能力。
介绍了一种基于概率统计的异常数据辨识方法,对辨识出的异常数据分别进行纵向、横向修正。结合某地方电网的实际负荷数据和气象数据,分析了短期负荷预测的影响因素,特别是气象条件以及气象条件的累积效应对短期负荷预测的影响。通过采用不同气象条件的“累积阈值函数”进行数值折算,从而可以根据气象数值的变化对这种累积效应进行定量描述。
在综合考虑相关影响因素的基础上,利用上述参数选择方法所确定的最小二乘支持向量机模型进行实例预测,验证了本文所建预测模型的有效性。
Load forecasting is one of the key tasks for scheduling and planning departments of power system and the accurate load forecasting is very important to guarantee the power system security, stability and economic operation, as well as increase the electricity sector under the economic and social benefits in the electricity market environment. In this paper, the relevant basis theories of load forecasting are introduced and existing short-term load forecasting methods are summed up. some important concepts and the main idea of statistical learning theory(SLT) and a new generic learning algorithm - support vector machine(SVM) in the theoretical framework of statistical learning theory are introduced, and a short-term load forecasting model is established based on least squares support vector machine(LS-SVM), which is an expansion of the standard SVM.
LS-SVM's model parameters have an important impact on learning ability, which is not yet determined by an unified and effective choice method. In this paper, a choice method of model parameters based on similar days and ant colony algorithm is proposed, that is, according to the features of different effect for load with different effect factors, some similar days are separated into training samples and test samples with an improved grey relational degree, and then a new global search algorithm -ant colony algorithm is implemented to optimize parameters. The practical example shows that it's more reasonable and enhances the generalization ability of the model using the method to choose parameters.
An identification method of abnormal datas based on statistical probability is introduced, and vertical and horizontal amendments are also given for the abnormal datas. An analysis is given on the effect factors of short-term load forecasting, and in particular weather conditions and the accumulated effects of weather conditions with the actual load datas and weather datas of a local power network. By using "accumulated threshold function" of different weather conditions to quantify the effect, the accumulated effects of weather conditions can be described with the changes of numerical value.
On the basis of related effect factors, a practical example is given using the LS-SVM's model which is constructed by the former choice method of model parameters, and the results show the effectiveness of the model in this paper.
引文
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