J
James Taylor
Researcher at Newcastle University
Publications - 1190
Citations - 43346
James Taylor is an academic researcher from Newcastle University. The author has contributed to research in topics: Laser & Fiber laser. The author has an hindex of 95, co-authored 1161 publications receiving 39945 citations. Previous affiliations of James Taylor include Institut national de la recherche agronomique & European Spallation Source.
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Journal ArticleDOI
Managing nitrogen to restore water quality in China
Chaoqing Yu,Xiao Huang,Han Chen,H. Charles J. Godfray,Jonathon S. Wright,Jim W. Hall,Peng Gong,Shaoqiang Ni,ShengChao Qiao,Guorui Huang,Yuchen Xiao,Jie Zhang,Zhao Feng,XiaoTang Ju,Philippe Ciais,Nils Chr. Stenseth,Nils Chr. Stenseth,Dag O. Hessen,Zhanli Sun,Le Yu,Wenjia Cai,Haohuan Fu,Xiaomeng Huang,Chi Zhang,Hongbin Liu,James Taylor +25 more
TL;DR: In this article, a combination of water-quality observations and simulated nitrogen discharge from agricultural and other sources was used to estimate spatial patterns of nitrogen discharge into water bodies across China from 1955 to 2014.
Journal ArticleDOI
Short-Term Load Forecasting Methods: An Evaluation Based on European Data
James Taylor,Patrick E. McSharry +1 more
TL;DR: In this article, a comparison of univariate methods for forecasting up to a day-ahead of electricity demand data from ten European countries is performed using intraday electricity demand from 10 European countries as the basis of an empirical comparison.
Journal ArticleDOI
A comparison of univariate methods for forecasting electricity demand up to a day ahead
TL;DR: In this article, the authors compared the performance of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead and concluded that simpler and more robust methods can outperform more complex alternatives.
Journal ArticleDOI
Neural network load forecasting with weather ensemble predictions
James Taylor,Roberto Buizza +1 more
TL;DR: In this paper, the authors investigated the use of weather ensemble predictions in the application of ANNs to load forecasting for lead times from one to ten days ahead and found that the average of the load scenarios is a more accurate load forecast than that produced using traditional weather forecasts.