Xinyu Zhao

TL;DR: The results on test set show the correction considering inherent errors of numerical techniques can integrate the physical with statistical information effectively and enhance the forecast accuracy indeed.

TL;DR: A novel data-driven method to realize short-term combination forecasts of average wind speed and wind turbulent standard deviation and one-dimensional convolutional neural network is innovatively applied for this work to excavate the timing coupled information in data.

TL;DR: This paper proposes the concept of gas turbine normal pattern extraction for the first time, extracts the unchanged feature of normal pattern from historical normal data and detects its changes for anomaly detection, and shows the proposed method is the optimal parameter combination for anomalies detection.

TL;DR: Experiments show that the proposed method can detect all 13 common gas path faults of three-shaft gas turbines sensitively while remaining low false alarm rate, and indicates the superiorities of data-driven methods and gas turbine principle fusion to some extent.

TL;DR: This approach is based on multi-task one-dimensional convolutional neural network including shared layer to extract information criteria-determined input correlations and task layer to fine-tune output accuracies and can provide reliable probabilistic manifold information for adjustable power scheduling.