智能用电环境下电力负荷预测方法的研究  

Power load forecasting method in smart electricity consumption environment

作　　者：马立新[1] 尹晶晶[1] 郑晓栋 栾健[1] 

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《机电工程》2015年第9期1233-1237,共5页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(61205076);上海市张江国家自主创新重点资助项目(201310-PI-B2-008)

摘　　要：针对在智能用电环境下研究对象复杂且负荷随机性强,短期电力负荷预测算法精度差、计算时间长等问题,提出一种基于ELM-Adaboost神经网络改进算法预测短期电力负荷的新方法。该方法引入Adaboost算法,首先对经过预处理后的历史数据进行测试样本权重初始化,然后反复训练ELM网络预测输出,ELM算法预测过程简洁,速度快;通过Adaboost算法调整测试样本权重并确定弱预测器权重,最后将得到的多个ELM弱预测器组成强预测器。实验以某市的电力负荷数据的进行预测仿真以及结果比较。仿真结果表明该方法具有较高的预测精度,泛化性能好,具有一定的理论意义和较好的应用前景。Aiming at solving the problems that the research objects are complex, the load randomness is strong and the short-term power load forecasting has low forecasting accuracy and long computation time in smart electricity consumption environment, a new power load forecasting method that based on extreme learning machine （ELM） and Adaboost algorithm was proposed. The Adaboost algorithm was extended to this method. Firstly, the historical data were processed, the sample weights were initialized and the ELM network was trained repeatedly to pre- dict output data. The forecasting process of ELM was simple and fast. The weight of weak predictors was identified through the Adaboost al- gorithm adjusting the weight of test samples. Finally the weak predictors were assembled together to constitute a strong predictor. The power load data of one city were used for simulating and comparing. The results indicate that this method not only has high prediction precision and good generalization performance, but also has a certain theoretical significance and good application prospect.

关 键 词：负荷预测 极限学习机 ADABOOST算法 强预测器 神经网络 

分 类 号：TM714[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]