Short term electricity load forecasting using a hybrid model

被引：204

作者：

Zhang, Jinliang ^{[1
]}

Wei, Yi-Ming ^{[2
]}

Li, Dezhi ^{[3
]}

Tan, Zhongfu ^{[1
]}

Zhou, Jianhua ^{[4
]}

机构：

[1] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China

[2] Beijing Inst Technol, Ctr Energy & Environm Policy Res, Beijing 100181, Peoples R China

[3] China Elect Power Res Inst, Beijing 100192, Peoples R China

[4] Shanghai Elect Power Co, Shanghai 200122, Peoples R China

来源：

ENERGY | 2018年 / 158卷

基金：

美国国家科学基金会;

关键词：

Electricity load forecasting; IEMD; ARIMA; WNN; FOA; SUPPORT VECTOR REGRESSION; WAVELET TRANSFORM; OPTIMIZATION ALGORITHM; COMBINATION MODEL; DECOMPOSITION; DEMAND; FRAMEWORK; GRIDS;

D O I：

10.1016/j.energy.2018.06.012

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Short term electricity load forecasting is one of the most important issue for all market participants. Short term electricity load is affected by natural and social factors, which makes load forecasting more difficult. To improve the forecasting accuracy, a new hybrid model based on improved empirical mode decomposition (IEMD), autoregressive integrated moving average (ARIMA) and wavelet neural network (WNN) optimized by fruit fly optimization algorithm (FOA) is proposed and compared with some other models. Simulation results illustrate that the proposed model performs well in electricity load forecasting than other comparison models. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：774 / 781

页数：8

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