【作者】 王晨枫；

【导师】 段玉波；

【作者基本信息】 大庆石油学院 ， 电力电子与电力传动， 2003， 硕士

【摘要】 电力系统负荷预测在电力系统安全、可靠和经济运行中起关键作用，它已逐步发展成为电力系统自动化领域中的重要研究方向之一。本文依托人工神经网络及遗传算法等理论方法，利用大庆油田电力总公司投入使用的远程电表集中抄收设备，对电力系统负荷的短期预测展开深入探索，论文主要完成以下研究工作。 基于对人工神经网络的基础理论的研究，利用多层前向网的通用逼近能力，论述了利用人工神经网络进行负荷预测的原理和方法，并建立了具体模型。 人工神经网络中的BP算法理论成熟但存在缺陷，从本质上来说它属于局部寻优算法，在存在较多局部极小的情况下很容易陷入局部极小点，且学习速度慢，不实用。针对这些问题，本文提出了利用遗传算法全局搜索能力强，结合神经网络的局部寻优能力，构成遗传神经网络。利用遗传算法对神经网络的结构及权值进行优化，从而达到加快寻优速度，提高训练精度的目的。 为使负荷预测的精度达到要求，对历史数据的精度有较高的要求。为减少坏数据对负荷预测的影响，本文提出了利用线性相位FIR滤波器对数据进行滤波的方法，经实际使用来看，FIR滤波器可有效滤除采样数据中的间断点。 将本文提出的人工神经网络模型应用于采油一厂的负荷预测，取得了较好的效果。更多还原

【Abstract】 Electric power load forecast is one of important factor for electric power system works safely, reliably and economically. It has been an main direction in the field of electric automation. In the article, we have relied on theory of Artificial Neural Networks (ANN) and Genetic Algorithms (GA), using the equipment of remote ammeter statistic system, and made a thorough research in the method of electric power short-term forecast. The main research has been finished as follows.On the base of basic theory research of ANN, using the general approach ability of Multi-layer Feedforward Neural Network, we dissertated the theory and method of load forecast with ANN, and built the model at the same time.BP algorithm is the mostly wide-used method in training ANN. But it is an local optimize method in nature, enter into local optimal point easily and training speed slowly. Aiming to these questions, we made use of the global optimal ability of GA, combining the local optimal ability of ANN, composing the GA-ANN. Using GA training the constructs and weight of ANN, We have gain the purpose of increasing optimal speed and raising precision.In order to increase the precision of forecast, The history data needs high precision. In the article, we brought forward the way of filtering the history data with FIR filter, so it can decrease the affection of bad data. In practice, FIR filter wiped off the interrupt point successfully.Applying the ANN model in this article to forecast the load of No 1 oil extraction plant, we gain good result.更多还原