2,299 research outputs found
Evaluation of simple wind power forecasting methods applied to a long-term wind record from Scotland
Lumping of Degree-Based Mean Field and Pair Approximation Equations for Multi-State Contact Processes
Contact processes form a large and highly interesting class of dynamic
processes on networks, including epidemic and information spreading. While
devising stochastic models of such processes is relatively easy, analyzing them
is very challenging from a computational point of view, particularly for large
networks appearing in real applications. One strategy to reduce the complexity
of their analysis is to rely on approximations, often in terms of a set of
differential equations capturing the evolution of a random node, distinguishing
nodes with different topological contexts (i.e., different degrees of different
neighborhoods), like degree-based mean field (DBMF), approximate master
equation (AME), or pair approximation (PA). The number of differential
equations so obtained is typically proportional to the maximum degree kmax of
the network, which is much smaller than the size of the master equation of the
underlying stochastic model, yet numerically solving these equations can still
be problematic for large kmax. In this paper, we extend AME and PA, which has
been proposed only for the binary state case, to a multi-state setting and
provide an aggregation procedure that clusters together nodes having similar
degrees, treating those in the same cluster as indistinguishable, thus reducing
the number of equations while preserving an accurate description of global
observables of interest. We also provide an automatic way to build such
equations and to identify a small number of degree clusters that give accurate
results. The method is tested on several case studies, where it shows a high
level of compression and a reduction of computational time of several orders of
magnitude for large networks, with minimal loss in accuracy.Comment: 16 pages with the Appendi
From local wind energy resource to national wind power production
Wind power is one of the most established renewable power resources yet it is also one of the most volatile resources. This poses a key challenge for successfully integrating wind power at a large scale into the power grid. Here we present an analysis of the time scales associated with wind power from hourly to seasonal fluctuations and how combining spatially distributed wind power sources helps to reduce its volatility. The analysis, based on observed wind speeds, is then generalised in a simple statistical model to develop a tool which can estimate the power output profile from a particular consortium of wind power sources. As the estimator only uses the local, or the mean national, wind resource and the mean distance between the sites to estimate the joint power output profile, it can be used by developers to estimate the reliability of their joint power output and to form the most effective consortium
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