Online 24-h solar power forecasting based on weather type classification using artificial neural network

被引：452

作者：

Chen, Changsong ^{[1
]}

Duan, Shanxu ^{[1
]}

Cai, Tao ^{[1
]}

Liu, Bangyin ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Coll Elect & Elect Engn, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China

来源：

SOLAR ENERGY | 2011年 / 85卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Power forecasting; Solar power; Neural network; Weather type; Photovoltaic power system; RADIATION; SYSTEM; MODEL;

D O I：

10.1016/j.solener.2011.08.027

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：

Power forecasting is an important factor for planning the operations of photovoltaic (PV) system. This paper presents an advanced statistical method for solar power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of solar irradiance, relative humidity and temperature at the site of the photovoltaic power system. A self-organized map (SOM) is trained to classify the local weather type of 24 h ahead provided by the online meteorological services. A unique feature of the method is that following a preliminary weather type classification, the neural networks can be well trained to improve the forecast accuracy. The proposed method is suitable for operational planning of transmission system operator, i.e. forecasting horizon of 24 h ahead and for PV power system operators trading in electricity markets. Application of the forecasting method on the power production of an actual PV power system shows the validity of the method. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：2856 / 2870

页数：15