Wind energy forecast in complex sites with a hybrid neural network and CFD based method

被引：22

作者：

Castellani, Francesco ^{[1
]}

Burlando, Massimiliano ^{[2
]}

Taghizadeh, Samad ^{[3
]}

Astolfi, Davide ^{[1
]}

Piccioni, Emanuele ^{[1
]}

机构：

[1] Univ Perugia, Dept Ind Engn, Via G Duranti 67, I-06125 Perugia, Italy

[2] Univ Genoa, DICCA, I-16126 Genoa, Italy

[3] Islamic Azad Univ, Dept Mfg Engn, Maraghah, Iran

来源：

ATI 2013 - 68TH CONFERENCE OF THE ITALIAN THERMAL MACHINES ENGINEERING ASSOCIATION | 2014年 / 45卷

关键词：

power forecast; Neural Networks; SCADA; wind energy; SPEED PREDICTION;

D O I：

10.1016/j.egypro.2014.01.021

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

The wind is an intermittent renewable energy source and the energy production forecast is a fundamental activity for many reasons (grid regulation, maintenance, etc..). In this work a hybrid method (based on weather forecast data, neural networks and computational fluid dynamics) and a pure neural network approach are compared in a complex terrain site. The post processing of real production data has been discovered to be a key activity. Treatment and filtering of data spreading out from the supervisory control and data acquisition system are fundamental both for training and testing methods reliability. (C) 2013 The Authors. Published by Elsevier Ltd.

引用

页码：188 / 197

页数：10

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