测控技术2018，Vol.37Issue(1)：59-63,5.
基于模块化回声状态神经网络光伏发电量预测
Forecasting of Photovoltaic Power Generation Based on Modular-Echo State Network
王大虎 1贾倩 2林红阳1
作者信息
- 1. 河南理工大学电气工程与自动化学院,河南焦作454000
- 2. 国网福建省电力有限公司经济技术研究院,福建福州350003
- 折叠
摘要
Abstract
In order to solve the security problems of the power system interconnection caused by uncertainty and intermittent of photovoltaic power generation,a model based on modular echo state network is proposed to forecast power generation.Firstly,the forecast sub-model was established according to the seasons by using modularized neural networks.Then,the sub-models were divided based on similar days from history data of photovoltaic power generation and together with the average temperature as samples to train the sub-model and forecast the power generation by the echo state network.Finally,the result was integrated output.The results show that this forecasting model has a small forecasting error when the day type is the same,while the prediction error is larger when the day type is different.However,compared with the ESN and BP prediction models,the forecasting model has higher forecasting precision and faster forecasting speed.关键词
回声状态网络/模块化神经网络/光伏发电/发电量预测Key words
echo state network/modularized neural networks/photovoltaic power generation/power generation forecasting分类
信息技术与安全科学引用本文复制引用
王大虎,贾倩,林红阳..基于模块化回声状态神经网络光伏发电量预测[J].测控技术,2018,37(1):59-63,5.
