21,286 research outputs found
Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems
Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions
Betting and Belief: Prediction Markets and Attribution of Climate Change
Despite much scientific evidence, a large fraction of the American public
doubts that greenhouse gases are causing global warming. We present a
simulation model as a computational test-bed for climate prediction markets.
Traders adapt their beliefs about future temperatures based on the profits of
other traders in their social network. We simulate two alternative climate
futures, in which global temperatures are primarily driven either by carbon
dioxide or by solar irradiance. These represent, respectively, the scientific
consensus and a hypothesis advanced by prominent skeptics. We conduct
sensitivity analyses to determine how a variety of factors describing both the
market and the physical climate may affect traders' beliefs about the cause of
global climate change. Market participation causes most traders to converge
quickly toward believing the "true" climate model, suggesting that a climate
market could be useful for building public consensus.Comment: All code and data for the model is available at
http://johnjnay.com/predMarket/. Forthcoming in Proceedings of the 2016
Winter Simulation Conference. IEEE Pres
EURACE: A Massively Parallel Agent-Based Model of the European Economy
EURACE is a major European attempt to construct an agent-based model of the European economy with a very large population of autonomous, purposive agents interacting in a complicated economic environment. To create it, major advances are needed, in particular in terms of economic modeling and software engineering.In this paper, we describe the general structure of the economic model developed for EURACE and present the Flexible Large-scale Agent Modeling Environment (FLAME) that will be used to describe the agents and run the model on massively parallel supercomputers. Illustrative simulations with a simplifiedmodel based on EURACE's labour market module are presented.Agent-based Computational Economics; X-Machines; Parallelcomputation.
Agent-Based Modeling: The Right Mathematics for the Social Sciences?
This study provides a basic introduction to agent-based modeling (ABM) as a powerful blend of classical and constructive mathematics, with a primary focus on its applicability for social science research.ļæ½ The typical goals of ABM social science researchers are discussed along with the culture-dish nature of their computer experiments. The applicability of ABM for science more generally is also considered, with special attention to physics. Finally, two distinct types of ABM applications are summarized in order to illustrate concretely the duality of ABM: Real-world systems can not only be simulated with verisimilitude using ABM; they can also be efficiently and robustly designed and constructed on the basis of ABM principles. ļæ½
Machine Learning for Smart and Energy-Efficient Buildings
Energy consumption in buildings, both residential and commercial, accounts
for approximately 40% of all energy usage in the U.S., and similar numbers are
being reported from countries around the world. This significant amount of
energy is used to maintain a comfortable, secure, and productive environment
for the occupants. So, it is crucial that the energy consumption in buildings
must be optimized, all the while maintaining satisfactory levels of occupant
comfort, health, and safety. Recently, Machine Learning has been proven to be
an invaluable tool in deriving important insights from data and optimizing
various systems. In this work, we review the ways in which machine learning has
been leveraged to make buildings smart and energy-efficient. For the
convenience of readers, we provide a brief introduction of several machine
learning paradigms and the components and functioning of each smart building
system we cover. Finally, we discuss challenges faced while implementing
machine learning algorithms in smart buildings and provide future avenues for
research at the intersection of smart buildings and machine learning
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
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