A Hybrid Krill-ANFIS Model for Wind Speed Forecasting

被引：15

作者：

Ahmed, Khaled ^{[1
,7
]}

Ewees, Ahmed A. ^{[2
,7
]}

Abd El Aziz, Mohamed ^{[1
,3
,7
]}

Hassanien, Aboul Ella ^{[4
,7
]}

Gaber, Tarek ^{[5
,7
]}

Tsai, Pei-Wei ^{[6
]}

Pan, Jeng-Shyang ^{[6
]}

机构：

[1] Cairo Univ, Fac Comp & Informat, Giza, Egypt

[2] Damietta Univ, Dept Comp, Dumyat, Egypt

[3] Zagazig Univ, Fac Sci, Zagazig, Egypt

[4] Nahda Univ, Fac Comp Sci, Bani Suwayf, Egypt

[5] Suez Canal Univ, Fac Comp & Informat, Ismailia, Egypt

[6] Fujian Univ Technol, Fujian Prov Key Lab Big Data Min & Applicat, Fuzhou, Peoples R China

[7] SRGE, Cairo, Egypt

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT SYSTEMS AND INFORMATICS 2016 | 2017年 / 533卷

关键词：

Renewable energy; Adaptive Neuro-Fuzzy Inference System; Wind speed forecasting; Krill herd optimization; Hybrid model; FUZZY INFERENCE SYSTEM; PREDICTION; OPTIMIZATION; NETWORK;

D O I：

10.1007/978-3-319-48308-5_35

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Finding an alternative renewable energy source instead of using traditional energy such as electricity or gas is an important research trend and challenge. This paper presents a new hybrid algorithm that uses Krill Herd (KH) optimization algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS) to be able to fit for wind speed forecasting, which is an essential step to generate wind power. ANFIS's parameters are optimized using KH. The proposed model called (Krill-ANFIS). This model is compared with three models basic ANFIS, PSO-ANFIS, and GA-ANFIS. Krill-ANFIS proved that it can be used as an efficient predictor for the wind speed as well as it can achieve high results and performance measures of root mean square error (RMSE), Coefficient of determination R-2 and average absolute percent relative error (AAPRE).

引用

页码：365 / 372

页数：8

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