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Multi-Granular Electricity Consumer Load Profiling for Smart Homes using a Scalable Big Data Algorithm
تولید گرانول الکتریکی مصرف کننده بار برای ساخت خانه های هوشمند با استفاده از یک الگوریتم داده های بزرگ مقیاس پذیر-2018 With rising electricity prices, there is a need to give consumers greater control over their energy
consumption. It is anticipated that such informed consumers in control of their consumption patterns
will contribute to reduced energy usage and thus a sustainable environment. Smart meter technology
in smart homes provides real-time information to customers through devices such as in-home displays
and web portals, and provide half-hourly consumption data to electricity distributors and retailers.
Such data enables the profiling of consumers making it possible to understand different life styles and
electricity usage behaviours to provide customised electricity billing. To obtain the anticipated benefit
from such highly granular and high frequency data, it is essential to have big data technologies which
can process such volumes of data in near real time. The research described in this paper focus on
addressing the key requirements of large volume data processing and making use of the highly
granular nature of the data. Adapting a new scalable algorithm introduced by the authors for big data
processing, this work demonstrates the practicality of processing large volumes of data at multiple
levels of granularity. The faster processing capacity makes it possible to continuously analyse
consumption data at frequent intervals as they are collected and at a highly granular level thus
providing a practical solution as a smart home application. The advantages of the technique is
demonstrated using electricity consumption data for 10,000 households for a year from an Australian
electricity retailer.
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