David Criado-Ramón

Orcid: 0000-0003-3030-792X

According to our database1, David Criado-Ramón authored at least 13 papers between 2022 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
A data-driven analysis to predict energetic intelligence.
Int. J. Data Sci. Anal., December, 2026

Leveraging classic and bio-inspired multi-objective metaheuristics for energy communities' optimal sizing.
Appl. Soft Comput., 2026

2025
Exploring multi-objective metaheuristics to optimise resource allocation in energy communities.
Computing, November, 2025

A parallel approach to accelerate neural network hyperparameter selection for energy forecasting.
Expert Syst. Appl., 2025

2024
An Application of Fuzzy Symbolic Time-Series for Energy Demand Forecasting.
Int. J. Fuzzy Syst., April, 2024

A Novel Non-Intrusive Load Monitoring Algorithm for Unsupervised Disaggregation of Household Appliances.
Inf., February, 2024

A GPU-accelerated adaptation of the PSO algorithm for multi-objective optimization applied to artificial neural networks to predict energy consumption.
Appl. Soft Comput., 2024

2023
Assessing the impact of soiling on photovoltaic efficiency using supervised learning techniques.
Expert Syst. Appl., November, 2023

CUDA-bigPSF: An optimized version of bigPSF accelerated with graphics processing Unit.
Expert Syst. Appl., November, 2023

Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption.
Mach. Learn. Knowl. Extr., March, 2023

Application of Fuzzy and Conventional Forecasting Techniques to Predict Energy Consumption in Buildings.
Int. J. Intell. Syst., 2023

An Improved Pattern Sequence-Based Energy Load Forecast Algorithm Based on Self-Organizing Maps and Artificial Neural Networks.
Big Data Cogn. Comput., 2023

2022
Electric demand forecasting with neural networks and symbolic time series representations.
Appl. Soft Comput., 2022


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