Multivariate Gaussian Process Incorporated Predictive Model for Stream Turbine Power Plant

一种用于植物育种长期多环境试验数据的贝叶斯更新框架

A Bayesian Updating Framework for Long-term Multi-Environment Trial Data in Plant Breeding

本文提出一种贝叶斯框架，整合历史数据以稳定方差估计并量化不确定性，通过贝叶斯线性混合模型和MCMC方法改进多环境试验数据的统计分析。

Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data

This article investigates the optimization of yaw control inputs of a nine-turbine wind farm. The wind farm is simulated using the high-fidelity simulator SOWFA. The optimization is performed with a modifier adaptation scheme based on Gaussian processes. Modifier adaptation corrects for the mismatch between plant and model and helps to converge to the actual plan optimum. In the case study the modifier adaptation approach is compared with the Bayesian optimization approach. Moreover, the use of two different covariance functions in the Gaussian process regression is discussed. Practical recomm…

可再生能源对热力发电的电网层级影响：效率、排放与灵活性

Grid-level impacts of renewable energy on thermal generation: efficiency, emissions and flexibility

本文基于美国七大平衡机构的固定效应面板回归，量化风光发电对煤电与气电的替代及排放影响，揭示电网灵活性和燃料结构决定可再生能源减排效益。

Structured Sparsity Modeling for Improved Multivariate Statistical Analysis based Fault Isolation

In order to improve the fault diagnosis capability of multivariate statistical methods, this article introduces a fault isolation framework based on structured sparsity modeling. The developed method relies on the reconstruction based contribution analysis and the process structure information can be incorporated into the reconstruction objective function in the form of structured sparsity regularization terms. The structured sparsity terms allow selection of fault variables over structures like blocks or networks of process variables, hence more accurate fault isolation can be achieved. Four …