Latent-Space Dynamics for Prediction and Fault Detection in Geothermal Power Plant Operations

Latent-Space Dynamics for Prediction and Fault Detection in Geothermal Power Plant Operations

Show other versions (1)

This paper presents a latent-space dynamic neural network (LSDNN) model for the multi-step-ahead prediction and fault detection of a geothermal power plant’s operation. The model was trained to learn the dynamics of the power generation process from multivariate time-series data and the effects of e...

Full description

Bibliographic Details
Main Authors: Cladouhos, T.T (Author), Jafarpour, B. (Author), Ling, W. (Author), Liu, Y. (Author), Young, R. (Author), Zia, J. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Backpropagation
Dynamic neural networks
Dynamics
fault detection
Fault detection
Faults detection
Forecasting
Geothermal fields
geothermal operations
Geothermal power plants
Latent space dynamic
latent space dynamics
Learn+
Multi-step-ahead predictions
neural network
Neural network model
Neural networks
Neural-networks
Power- generations
power plant
Power plant operations
Principal component analysis
Process monitoring
Space dynamics
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items