【作者】 张岭；

【导师】 杜欣慧；

【作者基本信息】 太原理工大学 ， 电力系统及其自动化， 2005， 硕士

【摘要】 电力系统短期负荷预测不仅是电网调度部门和规划设计部门所必须掌握的基本信息之一,也是电网的调度、运行及发展的重要依据。而且随着电力市场制度的完善,电网负荷的需求统计数据和预测数据将作为一项基本信息向全社会公布,它将成为电力市场实时交易中必不可少的重要组成部分。 电力系统短期负荷预测使用的方法有传统建模方法,诸如时间序列、回归分析等方法。由于负荷及影响负荷的因素间存在大量的非线性因素,上世纪90年代提出了用具有逼近任意非线性函数能力的神经网络进行短期负荷预测的方法。同期,其它的智能计算技术也在飞速发展,其中代表性的有遗传算法、模糊计算等。本文采用神经网络及神经网络与遗传算法相结合的方法进行短期负荷预测。 在进行短期负荷预测前,采用数学统计理论消除历史负荷数据中的不良数据,对历史负荷数据进行纵向、横向处理,剔除异点数据和平滑负荷曲线。鉴于城市居民生活负荷及商业负荷在太原市负荷中占有相当大的比例,它们对气象因素的变化非常敏感,因此在分别使用三层和四层神经网络进行负荷预测时,将其按照考虑气象因素与否分成了两种情况。因为神经网络收敛速度慢,容易陷入局部更多还原

【Abstract】 Short term load forecasting (STLF) of power system is not only the fundamental information of grid dispatching and scheduling departments, but also the foundation of grid dispatching, operation and development. Furthermore, with the progress of power market, demand statistic and forecasting data of grid load will be declared to all the communities and it will be one of important parts of power market real time trade.There are traditional model methods of forecasting short-term load, such as time series, regression analysis, and so on. Lots of non-linear relationships exist between load and factors that influence it. Artificial neural network (ANN) was put forward for forecasting short-term load in 1990s because of its ability to approach any non-linear functions. In the same term, the other intelligent computing technologies develop fast, such as genetic algorithm (GA), fuzzy computing. In the thesis, ANN and ANN combining with GA are adopted to forecastingshort-term load.Before forecasting short-term load, defective data are eliminated from historical records by mathematical statistical means, and historical data are pretreated lengthways and transversely to get rid of abnormal data and smooth load curve. Whereas urban resident and commercial load have considerable percentage of Taiyuan load and they are sensitive to weather factors. Therefore, when forecasting short-term load by three layers ANN and four layers ANN, it is sorted according to whether considering weather factors or not. Because ANN has local minimum value and its convergence speed is slow, ANN’s weight value and threshold value are confirmed by GA.Short-term load of Taiyuan area is forecasted by the above methods in the thesis and the summary is as follows: Though weather factors influence load, as far as Taiyuan grid is concerned that the results without weather factors are superior to those with weather factors when electricity is severe shortage; On the basis of lots of calculation, four layers ANN is superior to three layers ANN at the aspect of function mapping ability, and when ANN combining with GA, numbers of chromosome gene of four layers ANN are less than those of three layers ANN. So compared to three layers ANN, four layers ANN saves computing time much. When combining ANN with GA to keep ANN from falling into local minimumvalue, it is at the cost of increasing computing time; The research of load characteristic should be done well to confirm the relationships between load and the factors that influnce it, and to select better similar historical days.In a word, STLF in Taiyuan area is discussed in the thesis and there comes to the conclusion that four layers ANN combining with GA is valid. The next job is to reduce computing time and analyze load characteristic.更多还原