A5034384417- Energy Load and Power Forecasting
- Solar Radiation and Photovoltaics
- Air Quality Monitoring and Forecasting
- Wind and Air Flow Studies
- Hydrological Forecasting Using AI
- Photovoltaic System Optimization Techniques
- Meteorological Phenomena and Simulations
- Machine Learning and ELM
- Air Traffic Management and Optimization
- Neural Networks and Applications
- Remote Sensing and LiDAR Applications
- Species Distribution and Climate Change
- Flood Risk Assessment and Management
- Vehicle emissions and performance
- Atmospheric chemistry and aerosols
- Complex Systems and Time Series Analysis
- Time Series Analysis and Forecasting
- Fire effects on ecosystems
- Wind Energy Research and Development
- Image Enhancement Techniques
- Atmospheric Ozone and Climate
- Climate variability and models
- Fire Detection and Safety Systems
- Animal Vocal Communication and Behavior
- Complex Network Analysis Techniques
Universidad Politécnica de Madrid
2016-2025
Universidad de Valladolid
2011-2020
Universidad Complutense de Madrid
2011
Atmospheric low-visibility events are usually associated with fog formation. Extreme deeply affect the air and ground transportation, airports motor-road facilities causing accidents traffic problems every year. Machine Learning (ML) algorithms have been successfully applied to many formation prediction problems. The problem can be formulated either as a regression or classification task, which has an impact on type of ML approach used quality predictions obtained. In this paper we carry out...
Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing different one. The study systems' persistence involves definitions and uses techniques, depending on whether short-term or long-term considered. In this paper we discuss most definitions, concepts, methods, literature latest results systems. Firstly, used cases are presented. relevant methods characterize then discussed both cases. A complete...
Solar irradiation prediction is an important problem in geosciences with direct applications renewable energy. Recently, a high number of machine learning techniques have been introduced to tackle this problem, mostly based on neural networks and support vector machines. Gaussian process regression (GPR) alternative nonparametric method that provided excellent results other biogeophysical parameter estimation. In letter, we evaluate GPR for the estimation solar irradiation. Noting...
In this paper we propose and discuss different Deep Learning-based ensemble algorithms for a problem of low-visibility events prediction due to fog. Specifically, seven Learning (DL) architectures have been considered, from which multiple individual learners are generated. Hyperparameters the models, including parameters concerning data preprocessing, models architecture training procedure, randomly selected each model within pre-defined discrete range. Also, every is trained with slightly...
This paper presents a method for providing explainability in the integration of artificial intelligence (AI) and data mining techniques when dealing with meteorological prediction. Explainable (XAI) refers to transparency AI systems explanations their predictions decision-making processes, contribute improve prediction accuracy enhance trust systems. The focus this relies on interpretability challenges ordinal classification problems within weather forecasting. Ordinal involves predicting...
This paper presents long- and short-term analyses predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, persistence-based algorithms. On the other hand, reservoir modeled prediction problem, where machine learning regression were studied. A set including different types neural networks, Support Vector regression, or Gaussian processes tested. Real data from...
ABSTRACT An accurate prediction of wind power generation is crucial for optimizing the integration energy into grid, ensuring reliability. This research focuses on enhancing accuracy forecasts by combining data from mesoscale and reanalysis models with Machine Learning (ML) approaches. We utilized WRF forecast alongside ERA5 to estimate a farm located at Valladolid, Spain. The study evaluated performance ML based individually, as well combined model using inputs both datasets. hybrid...
Each year, wildfires destroy larger areas of Spain, threatening numerous ecosystems. Humans cause 90% them (negligence or provoked) and the behaviour individuals is unpredictable. However, atmospheric environmental variables affect spread wildfires, they can be analysed by using deep learning. In order to mitigate damage these events, we proposed novel Wildfire Assessment Model (WAM). Our aim anticipate economic ecological impact a wildfire, assisting managers in resource allocation...
Low visibility events are a severe problem for road transport, causing accidents and major economic losses. Their accurate prediction may help prevent these problems. For that purpose, machine deep learning techniques have been applied fog using in situ meteorological data persistence variables as baseline predictors. These evaluated different time-horizons: 1 h, 3 h 6 h. The effect of including extracted from ERA5 Reanalysis predictive has studied. A database, covering 23 months, used,...