Title :
Large Scale predictive analytics for real-time energy management
Author :
Balac, N. ; Sipes, Tamara ; Wolter, Nicole ; Nunes, Kenneth ; Sinkovits, Bob ; Karimabadi, Homa
Author_Institution :
Univ. of California, San Diego, La Jolla, CA, USA
Abstract :
As demand for cost-effective energy and resource management continues to grow, intelligent automated building solutions are necessary to reduce energy consumption, increase alternative energy sources, reduce operational costs and find interoperable solutions that integrate with legacy equipment without massive investments in new equipment and tools. The ability to analyze, understand and predict building behavior offer tremendous opportunities to demonstrate and validate increased energy efficiencies, which may ease many particular exorbitant pressures taxing the grid. In this paper, we describe a research platform driven by an existing campus microgrid for developing large scale, predictive analytics for real-time energy management.
Keywords :
building management systems; data analysis; energy consumption; power engineering computing; power grids; alternative energy sources; building behavior; campus microgrid; cost-effective energy; energy consumption; intelligent automated building solutions; interoperable solutions; large scale predictive analytics; legacy equipment; operational costs; real-time energy management; resource management; Buildings; Data models; Hidden Markov models; Mathematical model; Microgrids; Smart grids; Time series analysis; big data; data mining; smart grid; time series;
Conference_Titel :
Big Data, 2013 IEEE International Conference on
Conference_Location :
Silicon Valley, CA
DOI :
10.1109/BigData.2013.6691635