Ricardo Aler Mur

Associate Professor

Universidad Carlos III de Madrid

Spain

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About

Dr. Ricardo Aler Mur is an Associate Professor at Universidad Carlos III de Madrid, Spain. His research focuses on renewable energy modeling, particularly in the areas of solar and wind energy forecasting utilizing machine learning techniques. He has contributed to recent advancements in modeling renewable power production and improving prediction intervals for regional energy forecasts through innovative methodologies.

Articles (13)

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2025

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2025

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2023

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2023

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2023

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2022

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2022

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2022

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2021

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2021

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2020

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2020

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2020

Collaborators (4)

JAVIER HUERTAS-TATO

Universidad Politécnica de Madrid

SPAIN

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Esteban García Cuesta

Assistant Professor

Universidad Politécnica de Madrid

SPAIN

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David Pozo-Vazquez

Full Professor

Universidad de Jaén

SPAIN

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JOSE MARIA VALLS

Universidad Carlos III de Madrid

SPAIN

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Ricardo Aler Mur

Research Interests

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About

Articles (13)

A novel method for modeling renewable power production using ERA5: Spanish wind energy

A novel method for modeling renewable power production using ERA5: Spanish solar PV energy

Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques

Pareto Optimal Prediction Intervals with Hypernetworks

Deep neural networks for the quantile estimation of regional renewable energy production

A combination of supervised dimensionality reduction and learning methods to forecast solar radiation

Using a Multi-view Convolutional Neural Network to monitor solar irradiance

Direct estimation of prediction intervals for solar and wind regional energy forecasting with deep neural networks

Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types

Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems

A short-term solar radiation forecasting system for the Iberian Peninsula. Part 1: Models description and performance assessment

A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning

Study of Hellinger Distance as a splitting metric for Random Forests in balanced and imbalanced classification datasets

Collaborators (4)

JAVIER HUERTAS-TATO

Esteban García Cuesta

David Pozo-Vazquez

JOSE MARIA VALLS

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