Nonlinear Model-Based Predictive Control of Electric Water Heaters in Individual Dwellings

Created by W.Langdon from gp-bibliography.bib Revision:1.7975

@InProceedings{laguili:2024:CEC,

• author = "Oumaima Laguili and Julien Eynard and Stephane Grieu",
• title = "Nonlinear Model-Based Predictive Control of Electric Water Heaters in Individual Dwellings",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Water heating, Predictive models, Prediction algorithms, Solar panels, Solar heating, Greenhouse gases, Optimization, Domestic hot water, electric water heater, genetic algorithm, model-based predictive control, nonlinear optimization, rule-based control, solar photovoltaics",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611874",
• abstract = "The increasing use of photovoltaics (PV) in the residential sector aims at contributing to reducing greenhouse gas (GHG) emissions. However, one issue that slows down the adoption of the technology is the duck curve issue. A promising solution for addressing this issue is to use electric water heaters (EWHs) as a means of storing the PV power generation surplus - in buildings equipped with PV solar panels, a big part of the electricity produced is often not consumed locally - while preserving users' comfort. The primary goal of this research work encompass the development of EWH control strategies, including a model-based predictive control (MPC) strategy - the optimisation problem is solved using a genetic algorithm (GA) -, with the aim of improving both the PV power generation self-consumption (SC) rate and the economic gain. The MPC strategy outperforms the rule-based (RB) strategies.",
• notes = "also known as \cite{10611874}

  WCCI 2024",

}

Genetic Programming entries for Oumaima Laguili Julien Eynard Stephane Grieu

Citations