Artificial Intelligence-Driven Energy Optimization of Renewable-Powered Ozone Systems

ES Energy & Environments 2026 Open access

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Authors: Askar Abdykadyrov, Assel Zhumatova, Dana Adenova, Amangely Bekbayev, Yerkin Khidolda, Yerlan Sarsenbayev, Kalizhan Shakenov, Nurlan Kystaubayev
DOI: 10.30919/ee2210
Cited by: 1

Abstract

This study presents an AI-driven energy management framework for renewable-powered ozone generation systems based on dielectric barrier discharge (DBD) technology.The aim is to improve energy efficiency, operational reliability, and adaptability under variable renewable energy conditions.The proposed system integrates photovoltaic (PV) generation, battery storage, and an

Fields of Study & Subjects

Maritime Transport Emissions and Efficiency Advanced Multi-Objective Optimization Algorithms Integrated Energy Systems Optimization

Publication details

Journal / source
ES Energy & Environments
Publisher
Engineered Science Publisher
Publication date
1 January 2026