Julien Eynard

Associate Professor (Control Engineering)

Université de Perpignan Faculté de Sciences Exactes et Expérimentales

France

Research Interests

Explore related searches

Professors in France Professors in Energy Engineering Energy Engineering professors in France

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Julien Eynard is an Associate Professor in Control Engineering at Université de Perpignan Faculté de Sciences Exactes et Expérimentales, France. His research primarily focuses on model-based predictive control, energy management for microgrids, and forecasting techniques related to photovoltaic systems and irradiance. Recent contributions include advancements in predictive control applications for solar reactors and a comprehensive examination of machine learning methods in energy forecasting.

Articles (11)

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Collaborators (1)

Stéphane Grieu

Full Professor

Université de Perpignan Via Domitia

FRANCE

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Julien Eynard

Research Interests

Explore related searches

Contact this professor

About

Articles (11)

Model Predictive Control of Electric Water Heaters in Individual Dwellings Equipped with Grid-Connected Photovoltaic Systems

Intrahour Direct Normal Irradiance Forecasting Based on Sky Image Processing and Time-Series Analysis

Model-Based Predictive Control of a Solar Reactor

Predictive Energy Management of a Building-Integrated Microgrid: A Case Study

Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass

Comparison between Time- and Observation-Based Gaussian Process Regression Models for Global Horizontal Irradiance Forecasting

A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

Resilient Predictive Control Coupled with a Worst-Case Scenario Approach for a Distributed-Generation-Rich Power Distribution Grid

A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting

Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation

Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study

Collaborators (1)

Stéphane Grieu

Make your next move.