District Heating and Cooling Networks

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...

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Year of Publication:2020
Language:English
Physical Description:1 electronic resource (270 p.)
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520 |a 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, benefits would be experienced in the form of an increase in energy efficiency, an improvement in energy security, and a minimisation of emitted greenhouse gases. Given that heat demand is not expected to decrease significantly in the medium term, district heating networks show the greatest potential for the development of cogeneration. Due to their cost competitiveness, flexibility in terms of the ability to use renewable energy resources (such as geothermal or solar thermal) and fossil fuels (more specifically the residual heat from combustion), and the fact that, in some cases, losses to a country/region’s energy balance can be easily integrated into district heating networks (which would not be the case in a “fully electric” future), district heating (and cooling) networks and cogeneration could become a key element for a future with greater energy security, while being more sustainable, if appropriate measures were implemented. This book therefore seeks to propose an energy strategy for a number of cities/regions/countries by proposing appropriate measures supported by detailed case studies. 
546 |a English 
653 |a district heating 
653 |a 4th generation district heating 
653 |a data mining algorithms 
653 |a energy system modeling 
653 |a neural networks 
653 |a baseline model 
653 |a hydronic pavement system 
653 |a biomass district heating for rural locations 
653 |a CO2 emissions abatement 
653 |a low temperature networks 
653 |a ultralow-temperature district heating 
653 |a domestic 
653 |a optimization 
653 |a energy efficiency 
653 |a sustainable energy 
653 |a big data frameworks 
653 |a verification 
653 |a energy prediction 
653 |a parameter analysis 
653 |a greenhouse gas emissions 
653 |a time delay 
653 |a heat pumps 
653 |a primary energy use 
653 |a retrofit 
653 |a energy consumption forecast 
653 |a district heating (DH) network 
653 |a low-temperature district heating 
653 |a thermal inertia 
653 |a variable-temperature district heating 
653 |a data streams analysis 
653 |a Computational Fluid Dynamics 
653 |a energy management in renovated building 
653 |a Scotland 
653 |a heat reuse 
653 |a thermally activated cooling 
653 |a district cooling 
653 |a space cooling 
653 |a Gulf Cooperation Council 
653 |a biomass 
653 |a TRNSYS 
653 |a hot climate 
653 |a optimal control 
653 |a air-conditioning 
653 |a machine learning 
653 |a low temperature district heating system 
653 |a data center 
653 |a twin-pipe 
653 |a residential 
653 |a prediction algorithm 
653 |a CFD model 
653 |a nZEB 
653 |a thermal-hydraulic performance 
776 |z 3-03928-839-3 
700 1 |a Colmenar Santos, Antonio  |4 auth 
700 1 |a Rosales Asensio, Enrique  |4 auth 
906 |a BOOK 
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