【作者】 肖伟；

【导师】 罗滇生；

【作者基本信息】 湖南大学 ， 电气工程， 2008， 硕士

【摘要】 随着我国电力工业的发展和电网规模的扩大,电网的结构和运行方式变得越来越复杂。在电力市场化过程中,负荷预测的精度直接关系到各方利益。负荷的突然增加或者减少会对电力系统的安全运行带来不良的影响,因此有必要进行有效的负荷预测,提高超短期负荷预测的精度。超短期负荷预测通常是指预测一小时之内的电网负荷。其对电力系统控制、运行和计划都是非常重要的,提高其精度既能增强电力系统运行的安全性,又能改善电力系统运行的经济性。本文在对传统预测方法的算法进行分析的基础上,研究了三种新型超短期负荷预测方法。基于局部形相似的超短期负荷预测方法,该方法在对电力负荷局部特性分析的基础上,定义了负荷曲线形系数,该方法强调基于形相似基础进行值预测,克服了现有预测方法中对各相似日采用相同权重所导致的平滑效应对拐点负荷预测的影响。基于形态相似准则的曲线拟合算法,该方法以保证拟合曲线与实际曲线的形态最相似为准则,同时引入时间影响因子,将拟合曲线方程参数的求解转化为约束极值问题。该算法对超短期负荷预测中的曲线拟合预测方法进行了改进。基于元学习的时变非线性负荷预测组合算法,该算法在进行组合预测时将序列的特征属性和基预测器预测的结果形成元知识,作为元预测器的输入,从而发现并且纠正基预测器的系统偏差。同时通过门控网络确定各基预测器的权重。研究表明,前两种预测方法在保证运算速度的同时,提高了总体预测准确性和拐点处的预测准确性。第三种预测算法能纠正基预测器的系统偏差,提高超短期负荷预测的精度。更多还原

【Abstract】 Along with our country’s electric power industry developing and the electrical network’s expansive, the structure and the movement way of the electrical network are becoming more and more complex. In the course of electric power marketing, the accuracy of power load forecasting is directly related to the interests of all parties. The sudden increasement or the reduction both can bring bad influence to the electrical power system safe operation. It is the necessory to forecast the load effectively on line.Very short-term load forecasting means forecasting the load in an hour. It is very important to the control, the operation of the electrical power system. Improving its accuracy can strengthen the security and the efficiency of the electrical power system.This paper mainly analysises the Algorithmic structure, arithmetic speed, accuracy and error of tranditional methods, the three new very short-term load forecasting methods are studied.The first one is very short-term load forecasting method based on the local shape similarity. This mothod defines load curve shape coefficient based on analysis of power load local characteristic. It emphasizes on that the value forecasting should base on shape similarity. The influence of smoothing effect on inflexion point load forecasting caused by using homology weights commonly used in the existing forecasting methods is overcome.The second one is shape similarity criteyion based curve fitting algorithm. The criterion ensures the shape of the fitted curve should be most similar to that of actual curve is abided and at the same time the time-concerning impact factor is inducted, so the solution of parameters of fitting curve equation is transformed into constrained minimization problem. The proposed algorithm improves the curve fitting forecasting method in ultra-short-term load forecasting.The last one is time-varying nonlinear power load combined forecasting algorithm based on meta-learning. Meta knowledge formed by the results of base predictors and feature attributes of series is used as inputs of meta predictor when combined forecasting is applied. System bias can be founded and rectified. at the same time, the weights of base predictors are calculated using gating network. Results show that the first two method can improve forecasting precision including inflexion point, the third one can rectify system bias and improve forecasting accuracy in very short-term load forecasting.更多还原