Very Short-Term Wind Speed Forecasting Using Spatio-Temporal Lazy Learning

被引：5

作者：

Appice, Annalisa ^{[1
]}

Pravilovic, Sonja ^{[2
]}

Lanza, Antonietta ^{[1
]}

Malerba, Donato ^{[1
]}

机构：

[1] Univ Bari Aldo Moro, Dipartimento Informat, I-70126 Bari, Italy

[2] Mediterranean Univ, Fac Informat Technol, Podgorica 81000, Montenegro

来源：

DISCOVERY SCIENCE, DS 2015 | 2015年 / 9356卷

关键词：

D O I：

10.1007/978-3-319-24282-8_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

A wind speed forecast corresponds to an estimate of the upcoming production of a wind farm. The paper illustrates a variant of the Nearest Neighbor algorithm that yields wind speed forecasts, with a fast time resolution, for a (very) short time horizon. The proposed algorithm allows us to monitor a grid of wind farms, which collaborate by sharing information (i.e. wind speed measurements). It accounts for both spatial and temporal correlation of shared information. Experiments show that the presented algorithm is able to determine more accurate forecasts than a state-of-art statistical algorithm, namely auto. ARIMA.

引用

页码：9 / 16

页数：8

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