District Heating and Cooling Networks

• Hoofdauteur: Borge Diez, David (auth)
• Andere auteurs: Colmenar Santos, Antonio (auth), Rosales Asensio, Enrique (auth)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Onderwerpen: History of engineering & technology; district heating; 4th generation district heating; data mining algorithms; energy system modeling; neural networks; baseline model; hydronic pavement system; biomass district heating for rural locations; CO2 emissions abatement; low temperature networks; ultralow-temperature district heating; domestic; optimization; energy efficiency; sustainable energy; big data frameworks; verification; energy prediction; parameter analysis; greenhouse gas emissions; time delay; heat pumps; primary energy use; retrofit; energy consumption forecast; district heating (DH) network; low-temperature district heating; thermal inertia; variable-temperature district heating; data streams analysis; Computational Fluid Dynamics; energy management in renovated building; Scotland; heat reuse; thermally activated cooling; district cooling; space cooling; Gulf Cooperation Council; biomass; TRNSYS; hot climate; optimal control; air-conditioning; machine learning; low temperature district heating system; data center; twin-pipe; residential; prediction algorithm; CFD model; nZEB; thermal-hydraulic performance
• Online toegang: DOAB: download the publication; DOAB: description of the publication

Conventional thermal power generating plants reject a large amount of energy every year. If this rejected heat were to be used through district heating networks, given prior energy valorisation, there would be a noticeable decrease in the amount of fossil fuels imported for heating. As a consequence...