电力系统及其自动化学报2017，Vol.29Issue(8)：43-48,6.DOI:10.3969/j.issn.1003-8930.2017.08.007
基于大数据聚类的电力系统中长期负荷预测
Mid-long Term Load Forecasting of Power System Based on Big Data Clustering
摘要
Abstract
With the improvement of data collection ability,massive data of power load and related data are gradually ac-cumulated,which brings a new way for load forecasting. In this paper,a new method for mid-long term load forecasting is proposed based on big data technology. First,through the parametric expressions of the increasing trends and the vol-atility of historical load series,a standardized treatment of load is realized so as to form the samples of big data cluster-ing. Then,an improved fuzzy K-means algorithm based on MapReduce parallel programming model is designed with the combination of data processing ability of big data platform,thus the clustering analysis of big data of load is achieved. At last,forecasting models are built through synthesizing the same category of load. The calculating results show that the proposed algorithm can effectively accomplish the clustering analysis of massive load data,achieve a differentiated forecasting of load with diverse growth properties and improve the prediction accuracy.关键词
大数据/中长期负荷预测/聚类分析/MapReduce/并行编程Key words
big data/mid-long term load forecasting/clustering analysis/MapReduce/parallel programming分类
信息技术与安全科学引用本文复制引用
徐源,程潜善,李阳,张浩,余伟,何冰..基于大数据聚类的电力系统中长期负荷预测[J].电力系统及其自动化学报,2017,29(8):43-48,6.基金项目
国家自然科学基金资助项目(51107090) （51107090）
