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Found 18 papers, 8 papers with code
Adaptive Sparse Gaussian Process
Next, to make the model inference as simple as possible, we propose updating a single inducing point of the sparse GP model together with the remaining model parameters every time a new sample arrives.
Review of Kernel Learning for Intra-Hour Solar Forecasting with Infrared Sky Images and Cloud Dynamic Feature Extraction
The uncertainty of the energy generated by photovoltaic systems incurs an additional cost for a guaranteed, reliable supply of energy (i. e., energy storage).
Probabilistic analysis of solar cell optical performance using Gaussian processes
This work investigates application of different machine learning based prediction methodologies to estimate the performance of silicon based textured cells.
Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images
The optimal decision criterion to find the number of clusters in the mixture models is analyzed and compared between different Bayesian metrics and a sequential hidden Markov model.
Wind Flow Estimation in Thermal Sky Images for Sun Occlusion Prediction
Moving clouds affect the global solar irradiance that reaches the surface of the Earth.
Segmentation Algorithms for Ground-Based Infrared Cloud Images
The increasing number of Photovoltaic (PV) systems connected to the power grid are vulnerable to the projection of shadows from moving clouds.
Girasol, a Sky Imaging and Global Solar Irradiance Dataset
The energy available in Micro Grid (MG) that is powered by solar energy is tightly related to the weather conditions in the moment of generation.
Explicit Basis Function Kernel Methods for Cloud Segmentation in Infrared Sky Images
Photovoltaic systems are sensitive to cloud shadow projection, which needs to be forecasted to reduce the noise impacting the intra-hour forecast of global solar irradiance.
Processing of Global Solar Irradiance and Ground-Based Infrared Sky Images for Solar Nowcasting and Intra-Hour Forecasting Applications
We explain how to remove cyclostationary biases in global solar irradiance measurements.
Time Series Analysis
Instrumentation and Methods for Astrophysics
Image and Video Processing
Comparative Analysis of Methods for Cloud Segmentation in Ground-Based Infrared Images
The performances of supervised and unsupervised learning methods in cloud segmentation are evaluated.
An embedded deep learning system for augmented reality in firefighting applications
Firefighting is a dynamic activity, in which numerous operations occur simultaneously.
A conditional one-output likelihood formulation for multitask Gaussian processes
At the same time, by extending this approach with both a hierarchical and an approximate model, the proposed extensions are capable of recovering the multitask covariance and noise matrices after learning only $2T$ parameters, avoiding the validation of any model hyperparameter and reducing the overall complexity of the model as well as the risk of overfitting.
Time accelerated image super-resolution using shallow residual feature representative network
The recent advances in deep learning indicate significant progress in the field of single image super-resolution.
Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data
Koopman Mode Decomposition (KMD) is a technique of nonlinear time-series analysis that originates from point spectrum of the Koopman operator defined for an underlying nonlinear dynamical system.
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting
Intelligent detection and processing capabilities can be instrumental to improving the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings.
Unsupervised Segmentation of Fire and Smoke from Infra-Red Videos
This paper proposes a vision-based fire and smoke segmentation system which use spatial, temporal and motion information to extract the desired regions from the video frames.
Data Selection for Short Term load forecasting
Power load forecast with Machine Learning is a fairly mature application of artificial intelligence and it is indispensable in operation, control and planning.
A Unified SVM Framework for Signal Estimation
On the one hand, the signal model equation is written in reproducing kernel Hilbert spaces (RKHS) using the well-known RKHS Signal Model formulation, and Mercer's kernels are readily used in SVM non-linear algorithms.
