The growth of wind generation capacities in the past decades has shown that wind energy can contribute to the energy transition in many parts of the world. Being highly variable and complex to model, the quantification of the spatio-temporal variation of wind power and the related uncertainty is highly relevant for energy planners. Machine Learning has become a popular tool to perform wind-speed and power predictions. However, the existing approaches have several limitations. These include (i) insufficient consideration of spatio-temporal correlations in wind-speed data, (ii) a lack of existing methodologies to quantify the uncertainty of wind speed prediction and its propagation to the wind-power estimation, and (iii) a focus on less than hourly frequencies. To overcome these limitations, we introduce a framework to reconstruct a spatio-temporal field on a regular grid from irregularly distributed wind-speed measurements. After decomposing data into temporally referenced basis functions and their corresponding spatially distributed coefficients, the latter are spatially modelled using Extreme Learning Machines. Estimates of both model and prediction uncertainties, and of their propagation after the transformation of wind speed into wind power, are then provided without any assumptions on distribution patterns of the data. The methodology is applied to the study of hourly wind power potential on a grid of 250 by 250 squared meters for turbines of 100 meters hub height in Switzerland, generating the first dataset of its type for the country. The potential wind power generation is combined with the available area for wind turbine installations to yield an estimate of the technical potential for wind power in Switzerland. The wind power estimate presented here represents an important input for planners to support the design of future energy systems with increased wind power generation.
DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting
Department of Industrial Engineering, Clemson University（克莱姆森大学工业工程系）；Department of Computer Science, Rutgers University（罗杰斯大学计算机科学系）；Department of Statistics, Rutgers University（罗杰斯大学统计学系）；Department of Industrial & Systems Engineering, Rutgers University（罗杰斯大学工业与系统工程系）；University College, Korea University（韩国大学大学学院）
Spatiotemporal deep learning models for detection of rapid intensification in cyclones
Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India（印度理工学院班加罗尔分校土木工程系）；Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics（过渡人工智能研究组，数学与统计学学院）；University of New South Wales, Sydney, Australia（新南威尔士大学悉尼分校）；Centre for Artificial Intelligence and Innovation, Pingla Institute（人工智能与创新中心，Pingla研究所）
Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
Engineering and Technology Institute Groningen, University of Groningen（Groningen大学工程与技术学院）；Department of Human Movement Sciences, University of Groningen, University Medical Center Groningen（Groningen大学人类运动科学系）
Spatio-Temporal Attention Network for Epileptic Seizure Prediction
Department of Computer Science, Rensselaer Polytechnic Institute（计算机科学系，拉瑟拉尔理工学院）；IBM T.J. Watson Research Center（IBM 沃森研究中心）；Department of Neurology, Icahn School of Medicine at Mount Sinai（神经病学系，辛格恩医学院）
