A DEEP LEARNING MODEL FOR MINING OBJECT-ENERGY CORRELATIONS USING SOCIAL MEDIA IMAGE DATA

被引：0
|
作者：
Dering, Matthew [1 ]
Lee, Chonghan [1 ]
Hopkinson, Kenneth M. [2 ]
Tucker, Conrad S. [3 ]
机构：
[1] Penn State Univ, Comp Sci & Engn, University Pk, PA 16802 USA
[2] Air Force Inst Technol, Comp Sci, Dayton, OH USA
[3] Penn State Univ, Engn Design Ind & Mfg Engn, University Pk, PA 16802 USA
来源：
PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2018, VOL 1B | 2018年
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The authors of this work present a method that mines big media data streams from large Social Media Networks in order to discover novel correlations between objects appearing in images and electricity utilization patterns. The hypothesis of this work is that there exist correlations between what users take pictures of, and electricity utilization patterns. This work employs a Convolutional Neural Network to detect objects in 578,232 images gathered from over 15,000,000 tweets sent in the San Diego area. These objects were considered in the context of concurrent power use, on a monthly and hourly basis. The results reveal both positive and negative correlations between power use and specific objects, such as lamps(.053 hourly), dogs(-.011 hourly), horses(.422 monthly) and motorcycles(-.415, monthly).
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页数：11
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