2,429 research outputs found
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
NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring
Non-intrusive load monitoring, or energy disaggregation, aims to separate
household energy consumption data collected from a single point of measurement
into appliance-level consumption data. In recent years, the field has rapidly
expanded due to increased interest as national deployments of smart meters have
begun in many countries. However, empirically comparing disaggregation
algorithms is currently virtually impossible. This is due to the different data
sets used, the lack of reference implementations of these algorithms and the
variety of accuracy metrics employed. To address this challenge, we present the
Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed
specifically to enable the comparison of energy disaggregation algorithms in a
reproducible manner. This work is the first research to compare multiple
disaggregation approaches across multiple publicly available data sets. Our
toolkit includes parsers for a range of existing data sets, a collection of
preprocessing algorithms, a set of statistics for describing data sets, two
reference benchmark disaggregation algorithms and a suite of accuracy metrics.
We demonstrate the range of reproducible analyses which are made possible by
our toolkit, including the analysis of six publicly available data sets and the
evaluation of both benchmark disaggregation algorithms across such data sets.Comment: To appear in the fifth International Conference on Future Energy
Systems (ACM e-Energy), Cambridge, UK. 201
An IoT-based solution for monitoring a fleet of educational buildings focusing on energy efficiency
Raising awareness among young people and changing their behaviour and habits concerning energy usage iskey to achieving sustained energy saving. Additionally, young people are very sensitive to environmental protection so raising awareness among children is much easier than with any other group of citizens. This work examinesways to create an innovative Information & Communication Technologies (ICT) ecosystem (including web-based, mobile, social and sensing elements) tailored specifically for school environments, taking into account both theusers (faculty, staff, students, parents) and school buildings, thus motivating and supporting young citizenś behavioural change to achieve greater energy efficiency. A mixture of open-source IoT hardware and proprietary platforms on the infrastructure level, are currently being utilized for monitoring a fleet of 18 educational buildings across 3 countries, comprising over 700 IoT monitoring points. Hereon presented is the system's high-level architecture, as well as several aspects of its implementation, related to the application domain of educational building monitoring and energy efficiency. The system is developed based on open-source technologies andservices in order to make it capable of providing open IT-infrastructure and support from different commercial hardware/sensor vendors as well as open-source solutions. The system presented can be used to develop and offer newapp-based solutions that can be used either for educational purposes or for managing the energy efficiency ofthebuilding. The system is replicable and adaptable to settings that may be different than the scenarios envisionedhere (e.g., targeting different climate zones), different IT infrastructures and can be easily extended to accommodate integration with other systems. The overall performance of the system is evaluated in real-world environment in terms of scalability, responsiveness and simplicity
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
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