Analysis of Electricity Consumption Profiles by Means of Dimensionality Reduction Techniques

被引：0

作者：

Moran, Antonio ^{[1
]}

Fuertes, Juan J. ^{[1
]}

Prada, Miguel A. ^{[1
]}

Alonso, Serafin ^{[1
]}

Barrientos, Pablo ^{[1
]}

Diaz, Ignacio ^{[2
]}

机构：

[1] Esc Ing Ind & Informat, SUPPRESS Res Grp, Campus Vegazana S-N, Leon 24071, Spain

[2] Univ Oviedo, Dept Ing Eletr, Elect Computadores Sistemas, GijOn 33204, Spain

来源：

ENGINEERING APPLICATIONS OF NEURAL NETWORKS | 2012年 / 311卷

关键词：

Dimensionality reduction; information visualization; electricity consumption profiles;

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：

The analysis of the daily electricity consumption profile of a building and its correlation with environmental factors make it possible to estimate its electricity demand. As an alternative to the traditional correlation analysis, a new approach is proposed to provide a detailed and visual analysis of the correlations between consumption and environmental variables. Since consumption profiles are normally characterized by many electrical variables, i.e., a high dimensional space, it is necessary to apply dimensionality reduction techniques that enable a projection of these data onto an easily interpretable 2D space. In this paper, several dimensionality reduction techniques are compared in order to determine the most appropriate one for the stated purpose. Later, the proposed approach uses the chosen algorithm to analyze the profiles of two public buildings located at the University of Leon.

引用

页码：152 / +

页数：3

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