电力系统及其自动化学报2018，Vol.30Issue(2)：1-7,7.DOI:10.3969/j.issn.1003-8930.2018.02.001
基于泄漏积分型回声状态网络的在线学习光伏功率预测
Online-learning PV Power Forecasting Based on Leaky-integrator Echo State Network
摘要
Abstract
To enhance the accuracy of photovoltaic(PV)power forecasting,an online-learning method based on leaky-integrator echo state network(LIESN)is presented.In the forecasting model,leaky-integrator neurons are introduced to improve the short-term memory ability of the reservoir,and least square online-learning method is used to increase the influence of adjacent time samples on weights.With the comprehensive consideration of forecasting accuracy and run?ning time,the influences of key parameters of LIESN on the forecasting performance are analyzed,and a method is pro?posed to set the key parameters of LIESN.Practical examples indicate that the forecasting accuracy of online-learning LIESN was superior to those of BP neural network,plain ESN and offline-learning LIESN models,and the normalized root mean square error of the test result reached 0.0986,which verifies the validity of the proposed method.关键词
回声状态网络/泄漏积分/光伏功率预测/在线学习Key words
echo state network/leaky-integrator/PV power forecasting/online learning分类
信息技术与安全科学引用本文复制引用
徐正阳,路志英,刘洪..基于泄漏积分型回声状态网络的在线学习光伏功率预测[J].电力系统及其自动化学报,2018,30(2):1-7,7.基金项目
国家重大科学仪器设备开发专项资助项目(2013YQ03091510) （2013YQ03091510）
国家自然科学基金资助项目(51677123) （51677123）
