1 code implementation • 20 Feb 2023 • Vanessa Gómez-Verdejo, Emilio Parrado-Hernández, Manel Martínez-Ramón
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.
no code implementations • 11 Oct 2021 • Guillermo Terrén-Serrano, Manel Martínez-Ramón
The uncertainty of the energy generated by photovoltaic systems incurs an additional cost for a guaranteed, reliable supply of energy (i. e., energy storage).
no code implementations • 26 Jun 2021 • Rahul Jaiswal, Manel Martínez-Ramón, Tito Busani
This work investigates application of different machine learning based prediction methodologies to estimate the performance of silicon based textured cells.
1 code implementation • 7 May 2021 • Guillermo Terrén-Serrano, Manel Martínez-Ramón
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.
1 code implementation • 3 Mar 2021 • Guillermo Terrén-Serrano, Manel Martínez-Ramón
Moving clouds affect the global solar irradiance that reaches the surface of the Earth.
1 code implementation • 19 Feb 2021 • Guillermo Terrén-Serrano, Manel Martínez-Ramón
The increasing number of Photovoltaic (PV) systems connected to the power grid are vulnerable to the projection of shadows from moving clouds.
no code implementations • 15 Feb 2021 • Guillermo Terrén-Serrano, Adnan Bashir, Trilce Estrada, Manel Martínez-Ramón
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.
1 code implementation • 12 Feb 2021 • Guillermo Terrén-Serrano, Manel Martínez-Ramón
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.
2 code implementations • 21 Jan 2021 • Guillermo Terrén-Serrano, Manel Martínez-Ramón
We explain how to remove cyclostationary biases in global solar irradiance measurements.
Time Series Analysis
Instrumentation and Methods for Astrophysics
Image and Video Processing
1 code implementation • 13 Dec 2020 • Guillermo Terrén-Serrano, Manel Martínez-Ramón
The performances of supervised and unsupervised learning methods in cloud segmentation are evaluated.
no code implementations • 22 Sep 2020 • Manish Bhattarai, Aura Rose Jensen-Curtis, Manel Martínez-Ramón
Firefighting is a dynamic activity, in which numerous operations occur simultaneously.
1 code implementation • 5 Jun 2020 • Óscar García-Hinde, Vanessa Gómez-Verdejo, Manel Martínez-Ramón
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.
no code implementations • 8 Apr 2020 • Meenu Ajith, Aswathy Rajendra Kurup, Manel Martínez-Ramón
The recent advances in deep learning indicate significant progress in the field of single image super-resolution.
no code implementations • 4 Nov 2019 • Akitoshi Masuda, Yoshihiko Susuki, Manel Martínez-Ramón, Andrea Mammoli, Atsushi Ishigame
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.
no code implementations • 8 Oct 2019 • Manish Bhattarai, Manel Martínez-Ramón
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.
no code implementations • 18 Sep 2019 • Meenu Ajith, Manel Martínez-Ramón
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.
no code implementations • 2 Sep 2019 • Nestor Pereira, Miguel Angel Hombrados Herrera, Vanesssa Gómez-Verdejo, Andrea A. Mammoli, Manel Martínez-Ramón
Power load forecast with Machine Learning is a fairly mature application of artificial intelligence and it is indispensable in operation, control and planning.
no code implementations • 21 Nov 2013 • José Luis Rojo-Álvarez, Manel Martínez-Ramón, Jordi Muñoz-Marí, Gustavo Camps-Valls
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.