Real time energy measurement using smart meter

2016 ◽  
Author(s):  
Jayant P. Pawar ◽  
Amirthaganesh S. ◽  
ArunKumar S. ◽  
Satiesh Kumar B.
Keyword(s):  
Real Time ◽  
Energy Measurement ◽  
Smart Meter

scholarly journals Real-Time Analysis of Online Sources for Supporting Business Intelligence Illustrated with Bitcoin Investments and IoT Smart-Meter Sensors in Smart Cities

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1101 ◽  
Author(s):  
Iván García-Magariño ◽  
Moustafa M. Nasralla ◽  
Shah Nazir
Keyword(s):  
Data Management ◽  
Real Time ◽  
Business Intelligence ◽  
Smart Cities ◽  
Online Information ◽  
Smart Meter ◽  
Time Data ◽  
Real Time Analysis ◽  
Real Time Data ◽  
Historic Data

Real-time data management analytics involve capturing data in real-time and, at the same time, processing data in a light way to provide an effective real-time support. Real-time data management analytics are key for supporting decisions of business intelligence. The proposed approach covers all these phases by (a) monitoring online information from websites with Selenium-based software and incrementally conforming a database, and (b) incrementally updating summarized information to support real-time decisions. We have illustrated this approach for the investor–company field with the particular fields of Bitcoin cryptocurrency and Internet-of-Things (IoT) smart-meter sensors in smart cities. The results of 40 simulations on historic data showed that one of the proposed investor strategies achieved 7.96% of profits on average in less than two weeks. However, these simulations and other simulations of up to 69 days showed that the benefits were highly variable in these two sets of simulations (respective standard deviations were 24.6% and 19.2%).

scholarly journals Thwarting Nonintrusive Occupancy Detection Attacks from Smart Meters

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Dapeng Man ◽  
Wu Yang ◽  
Shichang Xuan ◽  
Xiaojiang Du
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Real World ◽  
Side Effect ◽  
Smart Meter ◽  
Smart Meters ◽  
Occupancy Detection ◽  
Load Monitoring ◽  
Privacy Issues ◽  
Statistical Metrics

Occupancy information is one of the most important privacy issues of a home. Unfortunately, an attacker is able to detect occupancy from smart meter data. The current battery-based load hiding (BLH) methods cannot solve this problem. To thwart occupancy detection attacks, we propose a framework of battery-based schemes to prevent occupancy detection (BPOD). BPOD monitors the power consumption of a home and detects the occupancy in real time. According to the detection result, BPOD modifies those statistical metrics of power consumption, which highly correlate with the occupancy by charging or discharging a battery, creating a delusion that the home is always occupied. We evaluate BPOD in a simulation using several real-world smart meter datasets. Our experiment results show that BPOD effectively prevents the threshold-based and classifier-based occupancy detection attacks. Furthermore, BPOD is also able to prevent nonintrusive appliance load monitoring attacks (NILM) as a side-effect of thwarting detection attacks.

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