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Recent developments in empirical dynamic modelling
Methods in Ecology and Evolution, 2023 Ecosystems are complex and sparsely observed making inference and prediction challenging. Empirical dynamic modelling (EDM) circumvents the need for a parametric model and complete observations of all system variables.
Stephan B. Munch +2 more
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Demand response of residential air conditioning load based on user behavior
Zhejiang dianli, 2023 Residential side demand response is an important supplementary means to maintain the supply-demand balance of source-load in the power system. However， the uncertainty of user behavior makes it difficult to accurately control demand response.
LIU Yiping +5 more
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Gaussian Process Time-Series Models for Structures under Operational Variability
Frontiers in Built Environment, 2017 A wide range of vibrating structures are characterized by variable structural dynamics resulting from changes in environmental and operational conditions, posing challenges in their identification and associated condition assessment. To tackle this issue,
Luis David Avendaño-Valencia +3 more
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Applied Sciences, 2019 Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of ...
Zhengmin Kong +3 more
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Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
Applied Sciences, 2021 Assuring the safety of automated vehicles is essential for their timely introduction and acceptance by policymakers and the public. To assess their safe design and robust decision making in response to all possible scenarios, new methods that use a ...
Felix Batsch +3 more
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Power Load Forecasting Method Based on MT-BSGP
Journal of Harbin University of Science and Technology, 2021 In order to forecast short-term household power load，a power load forecasting method based on multi-task Bayesian spatiotemporal Gaussian process ( MT-BSGP) is proposed.
LI Zhi-yong +5 more
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Multisensor Estimation Fusion with Gaussian Process for Nonlinear Dynamic Systems
Entropy, 2019 The Gaussian process is gaining increasing importance in different areas such as signal processing, machine learning, robotics, control and aerospace and electronic systems, since it can represent unknown system functions by posterior probability.
Yiwei Liao +3 more
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Conformations of Steroid Hormones: Infrared and Vibrational Circular Dichroism Spectroscopy
Molecules, 2023 Steroid hormone molecules may exhibit very different functionalities based on the associated functional groups and their 3D arrangements in space, i.e., absolute configurations and conformations.
Yanqing Yang +6 more
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Global Optimization Employing Gaussian Process-Based Bayesian Surrogates
Entropy, 2018 The simulation of complex physics models may lead to enormous computer running times. Since the simulations are expensive it is necessary to exploit the computational budget in the best possible manner.
Roland Preuss, Udo von Toussaint
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Journal of Asian Architecture and Building Engineering, 2021 With building energy codes getting strict, quantitative analysis is necessary in the early design stage of high-energy-performance buildings. To fully explore the design space, a highly efficient method is necessary.
Yun Gao +2 more
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