【作者】 赵洋；

【导师】 张重远；

【作者基本信息】 华北电力大学 ， 电气工程（专业学位）， 2016， 硕士

【摘要】 电力负荷预测是供电部门的重要工作之一,准确的负荷预测,可以经济合理的安排电网内部发电机组的起停,保持电网运行的安全稳定性,减少不必要的旋转备用容量,合理安排机组检修计划,保证社会的正常生产和生活,有效降低发电成本,提高经济效益和社会效益。负荷预测的结果,还可以有利于决定未来新的发电机组的安装,决定装机容量的大小、地点和时间,决定电网的建设和发展。本文基于数据挖掘技术的聚类分析技术,使用K-means聚类分析方法对历史负荷数据进行聚类分析,初始聚类个数设置为2,聚类结果与负荷中的工作日和节假日类型很好的对应。基于聚类分析结果,对实际负荷数据进行异常数据检测,对检测出的异常数据利用灰色理论的GM(1,1)模型实现了异常数据的修正。通过查询气象部门相应日期的气象数据,将当日的气象数据与步长为6的前推历史负荷值以及相应的聚类特征参数作为神经元输入,进行神经网络分析。最终实现整点负荷的预测。对南京江宁区实际负荷预测结果证明了本文所用方法的有效性。更多还原

【Abstract】 Power load forecasting is one of the important work of the power sector, accurate load forecast, can be economical and reasonable arrangements for the internal power generating unit start stop, to maintain security and stability of power grid operation, reduce unnecessary spinning reserve capacity, unit maintenance scheduling for reasonable arrangements to ensure the social normal production and life, effectively reduce the cost of power generation, increase the economic benefit and social benefit. The results of load forecasting, but also can help to determine the future of the new generation unit installation, decided to size, location and time of installed capacity, determine the construction and development of power grid.In this paper, based on data mining technique for clustering analysis technology, using k-means clustering analysis method is applied to clustering analysis of historical load data, the initial clustering number is set to 2, good clustering results with the heavy load of working day and holiday type counterpart. The results of cluster analysis based on the detection of abnormal data on actual load data to detect the abnormal data by using the grey theory GM(1, 1) model to achieve the correction of abnormal data. The meteorological department of meteorological data query response date, the meteorological data and the characteristic parameters for the clustering step 6 before pushing the historical load values and the corresponding neurons as input, neural network analysis. Finally realize the forecast of the whole point load. The effectiveness of the method is proved by the results of the actual load forecasting in Jiangning District of Nanjing.更多还原