Short-Term Load Forecasting Based on a Improved Deep Belief Network

被引：0
|
作者：
Zhang, Xiaoyu [1 ]
Wang, Rui [1 ]
Zhang, Tao [1 ]
Zha, Yabin [1 ]
机构：
[1] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha 410073, Hunan, Peoples R China
来源：
2016 INTERNATIONAL CONFERENCE ON SMART GRID AND CLEAN ENERGY TECHNOLOGIES (ICSGCE) | 2016年
基金：
美国国家科学基金会;
关键词：
short-term load forecast; deep belief network; scaled conjugate gradient algorithm; one step secant algorithm; Levenberg-Marquardt algorithm;
D O I：
暂无
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：
Short-term load forecast plays a vital role in the electric power industry. In this paper, a deep belief network is proposed for short-term load forecasting. However, the Back-Propagation (BP) algorithm of deep belief network for fine tuning has some inherent drawbacks and limitations, such as slow convergence rate and easy to fall into local minimum. To overcome the drawbacks, several neural network weight optimizing methods are proposed and compared. Experimental results show that Levenberg-Marquardt (LM) algorithm has the highest accuracy.
引用
收藏
页码：339 / 342
页数：4
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