TITLE:
Improved Short Term Energy Load Forecasting Using Web-Based Social Networks
AUTHORS:
Mehmed Kantardzic, Haris Gavranovic, Nedim Gavranovic, Izudin Dzafic, Hanqing Hu
KEYWORDS:
Short Term Energy Load Forecasting, Smart Grid, Social Networks, Event Detection
JOURNAL NAME:
Social Networking,
Vol.4 No.4,
October
30,
2015
ABSTRACT: 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.