Aman Mangal
Arun Mathew
Tanmay Randhavane
ABSTRACT
Smart grids are becoming ubiquitous today with proliferation of easy to install power generation schemes for Solar and Wind energy. The goal of consuming energy generated locally instead of transmitting it over large distances calls for systems that can process millions of events being generated from smart plugs and power generation sources in near real time. The heart of these systems often is a module that can process dense power consumption event streams and predict the consumption patterns at specific occupational units such as a house or a building. It is also often useful to identify outliers that are consuming power significantly higher than other similar devices in the occupational unit (such as a block or a neighbourhood). In this paper, we present a system that can process over a million events per second from smart plugs and correlate the information to output both accurate predictions as well as identify outliers. Our system is built from the ground up in C++ achieving very high throughput with very low CPU capacity for processing events. Our results show that the throughput of our system is over a million events per second while using under 20% of one core.
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Index Terms
- Predicting power needs in smart grids
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