电力系统保护与控制2012，Vol.40Issue(24)：95-99,5.
基于相空间重构和Chebyshev正交基神经网络的短期负荷预测
Short-term load forecasting based on phase space reconstruction and Chebyshev orthogonal basis neural network
杨胡萍 1王承飞 1朱开成 2胡奕涛1
作者信息
- 1. 南昌大学信息工程学院,江西南昌330031
- 2. 江西赣西供电公司,江西新余336500
- 折叠
摘要
Abstract
The electric power system short-term load data has obvious chaos characteristics. After talking about the related theory of phase space reconstruction in chaos, this paper calculates the delay time and embedded dimension needed in later example. According to orthogonal polynomial prediction's superior generalization and forecast performance, the paper constructs a single input neural network forecast model which is based on Chebyshev orthogonal basis after introducing Chebyshev orthogonal basis briefly. Because the point of every phase point in phase space reconstructed is more than one, the foregoing model can not meet the requirements. Therefore, the paper designs a multi input dynamic prediction model of Chebyshev orthogonal basis neural network based on phase space reconstruction. Through applying it to short-term load forecasting, the model gets a high precision and good prediction effect.关键词
混沌理论/相空间重构/Chebyshev/神经网络/短期负荷预测Key words
chaos theory/ phase space reconstruction/ Chebyshev/ neural network/ short-term load forecasting分类
信息技术与安全科学引用本文复制引用
杨胡萍,王承飞,朱开成,胡奕涛..基于相空间重构和Chebyshev正交基神经网络的短期负荷预测[J].电力系统保护与控制,2012,40(24):95-99,5.
