An Integrated Approach to Optimizing the Energy Efficiency of Water Supply—The Way to Achieve Effective Management

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F26%3A0201673" target="_blank" >RIV/00216305:26110/26:0201673 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2673-4591/69/1/212" target="_blank" >https://www.mdpi.com/2673-4591/69/1/212</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/engproc2024069212" target="_blank" >10.3390/engproc2024069212</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Integrated Approach to Optimizing the Energy Efficiency of Water Supply—The Way to Achieve Effective Management

Popis výsledku v původním jazyce

Escalating energy costs in recent years have spurred an examination of energy cost reduction strategies. Among these, exploring spot markets and building photovoltaic power plants at water facilities have emerged as promising options. This study focuses on the utilization of genetic algorithms to optimize pumping operations. Through simulations and case studies on small and medium-sized water distribution networks in the Czech Republic, the effectiveness of genetic algorithms at reducing operational costs is demonstrated. The integration of neural networks for predictive modeling and real-time decision-making complements the genetic algorithm approach, promising significant operational savings amid evolving energy market dynamics.

Název v anglickém jazyce

An Integrated Approach to Optimizing the Energy Efficiency of Water Supply—The Way to Achieve Effective Management

Popis výsledku anglicky

Escalating energy costs in recent years have spurred an examination of energy cost reduction strategies. Among these, exploring spot markets and building photovoltaic power plants at water facilities have emerged as promising options. This study focuses on the utilization of genetic algorithms to optimize pumping operations. Through simulations and case studies on small and medium-sized water distribution networks in the Czech Republic, the effectiveness of genetic algorithms at reducing operational costs is demonstrated. The integration of neural networks for predictive modeling and real-time decision-making complements the genetic algorithm approach, promising significant operational savings amid evolving energy market dynamics.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Engineering Proceedings

ISSN

—

e-ISSN

2673-4591

Svazek periodika

69

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

4

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105001401994