… machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear
power plant … , which include the supervised learning type, unsupervised learning type, and …

… Herein, we demonstrate the efficacy of several machine learning methods in by-passing …
power plant with five years of recorded data. We model the full load power output of the plant by …

… this paper is used to monitor the nuclear power plant. The plant model used in this work is a
… Developing robust fault diagnosis methods based on machine learning algorithms for NPPs …

… response of a virtual power plant using historic bidding and aggregated behaviour with
machine learning techniques. The two supervised machine learning techniques investigated and …

… A virtual power plant also includes secondary functionalities such as forecasting load, … with
virtual power plants, but these bodies of research are largely separate. Machine learning has …

… power plants using data-driven approaches is vital in reducing downtimes and increasing
efficiency. In this paper, three machine learning … The correlation coefficients between the plants…

… validated using a real dataset from a power transformer operating in a nuclear power plant.
… This paper focuses on nuclear power plants (NPPs) and the aging of transformers in this …

… Smart power plants are no longer just futuristic ideas but are rapidly becoming a reality, …
Machine Learning (ML) on edge, fog, and cloud levels of vertical IIoT concepts of power plants. A …

… machine learning regression methods for a prediction analysis of a thermodynamic system,
which is a combined cycle power plant (… Predicting electrical power output of a power plant …

… plant realization. We have found that project-level features have a larger impact on plant …
Applying the model to the pipeline of currently planned power plants, we have predicted the …