Forecasting Smart Meter Energy Usage Using Distributed Systems and Machine Learning

2018 ◽  
Author(s):  
Chris Dong ◽  
Lingzhi Du ◽  
Feiran Ji ◽  
Zizhen Song ◽  
Yuedi Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Distributed Systems ◽  
Smart Meter ◽  
Energy Usage

scholarly journals Avoiding Occupancy Detection From Smart Meter Using Adversarial Machine Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 35411-35430
Author(s):  
Ibrahim Yilmaz ◽  
Ambareen Siraj
Keyword(s):  
Machine Learning ◽  
Smart Meter ◽  
Occupancy Detection

Smart Metering for Applications

2014 ◽  
Vol 960-961 ◽  
pp. 823-827
Author(s):  
Ying Pan ◽  
Bo Jiang
Keyword(s):  
Smart Grid ◽  
Electrical Energy ◽  
Smart Meter ◽  
Smart Metering ◽  
Power Network ◽  
Energy Usage ◽  
Smart Meters ◽  
Additional Information ◽  
Energy Efficiency Improvement ◽  
Energy Meter

As an important part of Smart Grid, smart metering attracts more and more attention all over the world. It is the way for energy consumer to sense the benefit of smart grid directly. Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. This paper discusses various applications and technologies that can be integrated with a smart meter. Smart meters can be used not only from the supply side monitoring but also for the demand side management as well. It plays an important role to monitor the performance and the energy usage of the grid loadings and power quality. In addition, This paper gives a comprehensive view on the benefit of smart metering in power network such as energy efficiency improvement.

scholarly journals Machine learning based disaggregation of air-conditioning loads using smart meter data

2020 ◽  
Vol 14 (21) ◽  
pp. 4755-4762
Author(s):  
Behzad Najafi ◽  
Luca Di Narzo ◽  
Fabio Rinaldi ◽  
Reza Arghandeh
Keyword(s):  
Machine Learning ◽  
Air Conditioning ◽  
Smart Meter

Cryptanalysis and Improvement of Secure Key Distribution for the Smart Grid

2013 ◽  
Vol 479-480 ◽  
pp. 651-655
Author(s):  
Huei Ru Tseng ◽  
Tung Hung Chueh
Keyword(s):  
Smart Grid ◽  
Service Provider ◽  
Key Distribution ◽  
Distribution Networks ◽  
Smart Meter ◽  
Security Attacks ◽  
Energy Usage ◽  
Session Key ◽  
System Failures ◽  
Transmission And Distribution

The smart grid is a network of computers and power infrastructures that monitor and manage energy usage and uses intelligent transmission and distribution networks to deliver electricity for improving the electric system's reliability and efficiency. With grid controls, energy transmission management could be enhanced and resilience to control-system failures would be increased. Although deploying the smart grid has numerous social and technical benefits, several security concerns arise. In 2012, Xia and Wang proposed a secure key distribution for the smart grid. They claimed their protocol is strong enough to defend against security attacks. In this paper, we investigate the security of Xia and Wang's protocol. More precisely, we show that once the smart meter generates a session key with the service provider, the smart meter could easily forge the new legitimate session key without the service provider's participation. In order to remedy the security flaw, we propose a simple and secure improvement of Xia and Wang's protocol. Our protocol is secure and fair to generate the session key between the smart meter and the service provider.

Towards statistical modeling and machine learning based energy usage forecasting in smart grid

2015 ◽  
Vol 15 (1) ◽  
pp. 6-16 ◽  
Author(s):  
Wei Yu ◽  
Dou An ◽  
David Griffith ◽  
Qingyu Yang ◽  
Guobin Xu
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Statistical Modeling ◽  
Energy Usage

scholarly journals A distributed fMRI-based signature for the subjective experience of fear

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Feng Zhou ◽  
Weihua Zhao ◽  
Ziyu Qi ◽  
Yayuan Geng ◽  
Shuxia Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Distributed Systems ◽  
Negative Affect ◽  
Basal Forebrain ◽  
Subjective Experience ◽  
Neural Representation ◽  
Neural Systems ◽  
Subjective Feeling ◽  
Affective Neuroscience ◽  
Neural Signature

AbstractThe specific neural systems underlying the subjective feeling of fear are debated in affective neuroscience. Here, we combine functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n = 67), validation (n = 20) and generalization (n = 31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated ‘fear centers’.

scholarly journals Reducing energy usage in resource-intensive Java-based scientific applications via micro-benchmark based code refactorings

2019 ◽  
Vol 16 (2) ◽  
pp. 541-564
Author(s):  
Mathias Longo ◽  
Ana Rodriguez ◽  
Cristian Mateos ◽  
Alejandro Zunino
Keyword(s):  
Machine Learning ◽  
High Performance Computing ◽  
Computer Simulations ◽  
High Performance ◽  
Machine Learning Algorithms ◽  
Energy Usage ◽  
Energy Aware ◽  
Scientific Application ◽  
Performance Computing ◽  
Reducing Energy

In-silico research has grown considerably. Today?s scientific code involves long-running computer simulations and hence powerful computing infrastructures are needed. Traditionally, research in high-performance computing has focused on executing code as fast as possible, while energy has been recently recognized as another goal to consider. Yet, energy-driven research has mostly focused on the hardware and middleware layers, but few efforts target the application level, where many energy-aware optimizations are possible. We revisit a catalog of Java primitives commonly used in OO scientific programming, or micro-benchmarks, to identify energy-friendly versions of the same primitive. We then apply the micro-benchmarks to classical scientific application kernels and machine learning algorithms for both single-thread and multi-thread implementations on a server. Energy usage reductions at the micro-benchmark level are substantial, while for applications obtained reductions range from 3.90% to 99.18%.

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