The uncertainty of the energy generated by photovoltaic systems incurs an additional cost for a guaranteed, reliable supply of energy (i. e., energy storage).
This work investigates application of different machine learning based prediction methodologies to estimate the performance of silicon based textured cells.
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.
The increasing number of Photovoltaic (PV) systems connected to the power grid are vulnerable to the projection of shadows from moving clouds.
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.
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.
We explain how to remove cyclostationary biases in global solar irradiance measurements.
Time Series Instrumentation and Methods for Astrophysics Image and Video Processing
Firefighting is a dynamic activity, in which numerous operations occur simultaneously.
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.
The recent advances in deep learning indicate significant progress in the field of single image super-resolution.
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.
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.
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.
Power load forecast with Machine Learning is a fairly mature application of artificial intelligence and it is indispensable in operation, control and planning.
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.