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Advanced Computational Electricity Systems(ACES)

RESEARCH

GRID LEARNING

Zhengkai Wu and Michael Jezierny

Learning visualization is the process through which humans learn while immersed in complex visual analytics. The impetus for learning visualization is in complexity and the development of distributed autonomous controls for the emerging electric grid. Since the electricity end user (commercial, industrial, or residential) is interested in energy optimization and efficiency, but not in the intricate optimization and analytical processes, the logical technological step is to deploy distributed autonomous agents that have the responsibility of control driven by loose directives from the energy user. Learning visualization in multi-dimensional, multi-scale systems is essential to formalize the operational intelligence required to deploy embedded software for grid autonomous agents.

 

 

GRID LEARNING VISUALIZATION

Massive deployment of advanced sensors (intelligent electronic devices) at all levels of the electricity infrastructure is producing large amounts of data streams. This volume of data has tremendous scientific value that is currently under-utilized. Human analysts face significant challenges in synthesizing and assimilating these complex multivariate and spatiotemporally heterogeneous data collections. Existing analysis tools in the electricity industry are currently well below the needs.

Learning

 

Last revised on Aug. 25, 2011