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proposed a technique to similar group customers for
forecasting their energy profiles (Albert and Rajago-
pal, 2013).
Queuing-Based energy consumption management
system for residential smart gird is proposed by Yi Liu
et al. (Liu et al., 2016). Mainly there are two types
of demands, essential and flexible demands. Flexible
demands includes delay-sensitive and delay-tolerant
loads. These loads can be controled by residential
smart grid directly and scheduled accordingly. Which
means they need extra resources for controlling these
appliances, for millions of customer it’s not a feasible
solution.
6 CONCLUSION AND FUTURE
WORK
We have introduced SLS to overcome the issues with
conventional DR and DSM techniques and to manage
the variability factor of renewable energy resources.
The main idea behind SLS is to manage the variabi-
lity factor of renewable resources while keeping fair-
ness and customer inconvenience in view. Our future
work involves finding the most suitable balance be-
tween forcing and requesting the customers to shed
their energy usage at the time of need. Moreover,
since SLS depends on the forecasting of individual
customers’ energy profiles, we will be working to im-
prove this forecasting. While consumer level forecas-
ting techniques claim to have 80% accuracy, our goal
is to use contextual information from customers to im-
prove forecasting further. Smart grid testbed (Tushar
et al., 2016) could be use to test our solution and will
help us to imporve our technique.
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