LSSA-BP-based cost forecasting for onshore wind power

被引:16
作者
Feng, Ren [1 ]
Wencheng, Liu [1 ]
机构
[1] North China Elect Power Univ, Dept Econ Management, Baoding 071003, Hebei, Peoples R China
关键词
Wind power project; Cost prediction; Sparrow search algorithm; BP neural network model;
D O I
10.1016/j.egyr.2022.11.196
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
An LSSA-BP neural network prediction model was established for more accurate onshore wind power cost prediction. Optimise the weights and thresholds of the BP neural network using the sparrow search algorithm. Comparison of the traditional BP model, GA-BP model and LSSA-BP model to verify the superiority of the LSSA-optimised BP model. Moreover, using LSSA-BP in compared with Support Vector Regression Forecasting (SVR) and Random Forest Regression Forecasting (RFR) models. The results of model trial calculations and analysis showed that the LSSA-BP model had the highest prediction accuracy and could be used as a reference for the onshore wind power cost prediction.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:362 / 370
页数:9
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