Towards the Development of the Smart Grid: Fast Electricity Load Forecasting Using Different Hybrid Approaches

被引：0

作者：

Jurado, Sergio ^{[1
]}

Mugica, Francisco ^{[2
]}

Nebot, Angela ^{[2
]}

Avellana, Narcis ^{[1
]}

机构：

[1] Sensing & Control Syst SL, Barcelona, Spain

[2] Univ Politecn Cataluna, Dept Llenguatges i Sistemes InformAt, Barcelona, Spain

来源：

ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE OF THE CATALAN ASSOCIATION FOR ARTIFICIAL INTELLIGENCE | 2013年 / 256卷

关键词：

Smart Grid; Load Forecasting; Random Forest; Artificial Neural Networks; Fuzzy Inductive Reasoning; Hybrid Approach;

D O I：

10.3233/978-1-61499-320-9-185

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Currently, worldwide scientific community is doing a great effort of research in the area of Smart Grids because energy production, distribution, and consumption play a critical role in the sustainability of the planet. In this context, electricity load forecasting methodologies with fast response is a key component for demand-side management and the emergence of prosumers in the electricity grid. In this research it is shown that the computational intelligence techniques presented can deal with real time forecast, cope with incomplete measurement data and forecast signals of great variability, when applied to three real locations, with distinctly different characteristics.

引用

页码：185 / 188

页数：4

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