04273nam 2200937z- 450 991040408360332120231214132841.03-03928-840-7(CKB)4100000011302302(oapen)https://directory.doabooks.org/handle/20.500.12854/45291(EXLCZ)99410000001130230220202102d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierDistrict Heating and Cooling NetworksMDPI - Multidisciplinary Digital Publishing Institute20201 electronic resource (270 p.)3-03928-839-3 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.district heating4th generation district heatingdata mining algorithmsenergy system modelingneural networksbaseline modelhydronic pavement systembiomass district heating for rural locationsCO2 emissions abatementlow temperature networksultralow-temperature district heatingdomesticoptimizationenergy efficiencysustainable energybig data frameworksverificationenergy predictionparameter analysisgreenhouse gas emissionstime delayheat pumpsprimary energy useretrofitenergy consumption forecastdistrict heating (DH) networklow-temperature district heatingthermal inertiavariable-temperature district heatingdata streams analysisComputational Fluid Dynamicsenergy management in renovated buildingScotlandheat reusethermally activated coolingdistrict coolingspace coolingGulf Cooperation CouncilbiomassTRNSYShot climateoptimal controlair-conditioningmachine learninglow temperature district heating systemdata centertwin-piperesidentialprediction algorithmCFD modelnZEBthermal-hydraulic performanceBorge Diez Davidauth1312856Colmenar Santos AntonioauthRosales Asensio EnriqueauthBOOK9910404083603321District Heating and Cooling Networks3031034UNINA