D
Damien Fay
Researcher at Bournemouth University
Publications - 56
Citations - 782
Damien Fay is an academic researcher from Bournemouth University. The author has contributed to research in topics: Network topology & Internet topology. The author has an hindex of 14, co-authored 55 publications receiving 711 citations. Previous affiliations of Damien Fay include University of Cambridge & National University of Ireland, Galway.
Papers
More filters
Journal ArticleDOI
On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models
Damien Fay,John V. Ringwood +1 more
TL;DR: It is shown that the lowest PMSE corresponds to training the sub-models with actual weather but training the combiner with forecast weather, and the parameter estimation may thus be split into two parts: sub-model and combination parameter estimation.
Journal ArticleDOI
Weighted spectral distribution for internet topology analysis: theory and applications
Damien Fay,Hamed Haddadi,Andrew Thomason,Andrew W. Moore,Richard Mortier,Almerima Jamakovic,Steve Uhlig,Miguel Rio +7 more
TL;DR: A new metric is derived that enables exactly such a structural comparison: the weighted spectral distribution, which is used to quantify the effect of changing the mixing properties of a simple synthetic network generator.
Journal ArticleDOI
Determining association networks in social animals: Choosing spatial-temporal criteria and sampling rates
Hamed Haddadi,Andrew J. King,Alison Wills,Damien Fay,J. C. Lowe,A. Jennifer Morton,Stephen Hailes,Alan M. Wilson +7 more
TL;DR: This paper demonstrates how researchers can use experimental and statistical methods to establish spatial and temporal association patterns and thus correctly characterise social networks in both time and space and indicates the need for sampling intervals of less than a minute apart.
Journal ArticleDOI
24-h electrical load data—a sequential or partitioned time series?
TL;DR: This paper examines which approach is appropriate for forecasting hourly electrical load in Ireland and finds that, with the exception of some hours of the day, the sequential approach is superior.
Proceedings ArticleDOI
Network growth and the spectral evolution model
TL;DR: A link prediction algorithm based on the extrapolation of a network's spectral evolution, which shows that it performs particularly well for networks with irregular, but spectral, growth patterns.