Predicting Solar Power Output using Complex Fuzzy Logic

被引：0

作者：

Yazdanbaksh, Omolbanin ^{[1
]}

Krahn, Alix ^{[1
]}

Dick, Scott ^{[1
]}

机构：

[1] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada

来源：

PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS) | 2013年

关键词：

NEURAL-NETWORKS; SYSTEM; ANFIS; SETS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Photovoltaic (PV) power is one of the most promising renewable energy sources. However, it is also intermittent, and thus short-term forecasts of PV power generation are needed to integrate PV power into the electricity grid. This article compares two existing machine-learning approaches for forecasting (ANFIS and radial basis function networks) against a new approach based on complex fuzzy logic (ANCFIS). The proposed approach was more accurate in predicting power output one minute in advance on a simulated solar cell.

引用

页码：1243 / 1248

页数：6

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