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摘要:
In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.
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篇名 Improved Short Term Energy Load Forecasting Using Web-Based Social Networks
来源期刊 社交网络(英文) 学科 医学
关键词 SHORT TERM Energy Load Forecasting Smart Grid Social Networks EVENT Detection
年,卷(期) 2015,(4) 所属期刊栏目
研究方向 页码范围 119-131
页数 13页 分类号 R73
字数 语种
DOI
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研究主题发展历程
节点文献
SHORT
TERM
Energy
Load
Forecasting
Smart
Grid
Social
Networks
EVENT
Detection
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
社交网络(英文)
季刊
2169-3285
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
112
总下载数(次)
0
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