16,130 research outputs found
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
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Model-Driven Analytics of Energy Meter Data in Smart Homes
The proliferation of smart meter deployments has led to significant interest in analyzing home energy use as part of the emerging \u27smart grid\u27. As buildings account for nearly 40% of society\u27s energy use, data from smart meters provides significant opportunities for both utilities and consumers to optimize energy use, minimize waste, and provide insight into how modern homes and devices use energy. Meter data is often difficult to analyze, however, owing to the aggregation of many disparate and complex loads as well as relatively coarse measurement granularities. At utility scales, analysis is further complicated by the vast quantity of data, which precludes the use of computationally intensive techniques when monitoring hundreds or even thousands of homes.
In this thesis, I present an architecture for enabling smart homes using smart energy meters, encompassing efficient data collection and analysis to understand the behavior of home devices. I consider four primary challenges within this domain: (1) providing low-overhead data collection and processing for many devices, (2) designing models characterizing the energy use of modern devices, (3) using these models to track the real-time behavior of known devices, and (4) automatic identification of unknown devices in the home.
To enable practical smart homes, my proposed architecture combines low-cost, off-the-shelf sensing equipment with a hybrid local and cloud-based processing backend. To analyze data within the environment, I first characterize the basic device types present in today\u27s homes (e.g., resistive, inductive, or non-linear) and distill the essential usage characteristics of each type. Using these characteristics, I construct a set of models that more accurately represents real-world devices than previous simplistic models. I then leverage this modeling framework to track the behavior of specific devices, using a technique that runs in close to real-time and can scale to many devices. Finally, I present a technique to automatically identify unknown devices attached to smart outlets in homes, which relieves homeowners of the need to manually describe devices in order to employ smart home optimizations
TechNews digests: Jan - Mar 2010
TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month
Managing big data experiments on smartphones
The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones
Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments
The increased penetration of uncertain and variable renewable energy presents
various resource and operational electric grid challenges. Micro-level
(household and small commercial) demand-side grid flexibility could be a
cost-effective strategy to integrate high penetrations of wind and solar
energy, but literature and field deployments exploring the necessary
information and communication technologies (ICTs) are scant. This paper
presents an exploratory framework for enabling information driven grid
flexibility through the Internet of Things (IoT), and a proof-of-concept
wireless sensor gateway (FlexBox) to collect the necessary parameters for
adequately monitoring and actuating the micro-level demand-side. In the summer
of 2015, thirty sensor gateways were deployed in the city of Managua
(Nicaragua) to develop a baseline for a near future small-scale demand response
pilot implementation. FlexBox field data has begun shedding light on
relationships between ambient temperature and load energy consumption, load and
building envelope energy efficiency challenges, latency communication network
challenges, and opportunities to engage existing demand-side user behavioral
patterns. Information driven grid flexibility strategies present great
opportunity to develop new technologies, system architectures, and
implementation approaches that can easily scale across regions, incomes, and
levels of development
JUNO Conceptual Design Report
The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine
the neutrino mass hierarchy using an underground liquid scintillator detector.
It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants
in Guangdong, China. The experimental hall, spanning more than 50 meters, is
under a granite mountain of over 700 m overburden. Within six years of running,
the detection of reactor antineutrinos can resolve the neutrino mass hierarchy
at a confidence level of 3-4, and determine neutrino oscillation
parameters , , and to
an accuracy of better than 1%. The JUNO detector can be also used to study
terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard
Model. The central detector contains 20,000 tons liquid scintillator with an
acrylic sphere of 35 m in diameter. 17,000 508-mm diameter PMTs with high
quantum efficiency provide 75% optical coverage. The current choice of
the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO
as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of
detected photoelectrons per MeV is larger than 1,100 and the energy resolution
is expected to be 3% at 1 MeV. The calibration system is designed to deploy
multiple sources to cover the entire energy range of reactor antineutrinos, and
to achieve a full-volume position coverage inside the detector. The veto system
is used for muon detection, muon induced background study and reduction. It
consists of a Water Cherenkov detector and a Top Tracker system. The readout
system, the detector control system and the offline system insure efficient and
stable data acquisition and processing.Comment: 328 pages, 211 figure
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