Distributed Data Mining for Sustainable Smart Grids

Electric power infrastructure is rapidly running up against oversized growth, scale and efficiency. Electricity production, distribution and consumption play a critical role in the sustainability of the planet and its natural resources. Smart Grids which enable two-way communication and monitoring between producers and end-users need novel computational algorithms for supporting generation of power from wide range of sources, efficient energy distribution, and sustainable consumption. This paper explores fundamentally distributed approaches with more local flexibility leading to sustainable methodology compared to the traditional centralized frameworks for analyzing and processing data. The paper consider the problems of aggregation and prediction of power generation and consumption trends over a distributed smart grid. The need for more local control, privacy issues, and cost sensitivity for transmission of remote sensory data over the low-bandwidth wireless network is leading toward more distributed approach to data analysis in smart grids. This paper reviews our recent work on more sustainable distributed asynchronous methodology for constructing energy demand prediction models in a smart grid by multivariate linear regression as well a dynamic pricing model built on distributed rank aggregation that will help shape power consumption and optimize the grid.