LEADER 04273nam 2200937z- 450 001 9910404083603321 005 20231214132841.0 010 $a3-03928-840-7 035 $a(CKB)4100000011302302 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/45291 035 $a(EXLCZ)994100000011302302 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDistrict Heating and Cooling Networks 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (270 p.) 311 $a3-03928-839-3 330 $aConventional 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. 610 $adistrict heating 610 $a4th generation district heating 610 $adata mining algorithms 610 $aenergy system modeling 610 $aneural networks 610 $abaseline model 610 $ahydronic pavement system 610 $abiomass district heating for rural locations 610 $aCO2 emissions abatement 610 $alow temperature networks 610 $aultralow-temperature district heating 610 $adomestic 610 $aoptimization 610 $aenergy efficiency 610 $asustainable energy 610 $abig data frameworks 610 $averification 610 $aenergy prediction 610 $aparameter analysis 610 $agreenhouse gas emissions 610 $atime delay 610 $aheat pumps 610 $aprimary energy use 610 $aretrofit 610 $aenergy consumption forecast 610 $adistrict heating (DH) network 610 $alow-temperature district heating 610 $athermal inertia 610 $avariable-temperature district heating 610 $adata streams analysis 610 $aComputational Fluid Dynamics 610 $aenergy management in renovated building 610 $aScotland 610 $aheat reuse 610 $athermally activated cooling 610 $adistrict cooling 610 $aspace cooling 610 $aGulf Cooperation Council 610 $abiomass 610 $aTRNSYS 610 $ahot climate 610 $aoptimal control 610 $aair-conditioning 610 $amachine learning 610 $alow temperature district heating system 610 $adata center 610 $atwin-pipe 610 $aresidential 610 $aprediction algorithm 610 $aCFD model 610 $anZEB 610 $athermal-hydraulic performance 700 $aBorge Diez$b David$4auth$01312856 702 $aColmenar Santos$b Antonio$4auth 702 $aRosales Asensio$b Enrique$4auth 906 $aBOOK 912 $a9910404083603321 996 $aDistrict Heating and Cooling Networks$93031034 997 $aUNINA