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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| Format: | Book Chapter |
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MDPI - Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 04340naaaa2200889uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_45291 | ||
| 005 | 20210211 | ||
| 020 | |a books978-3-03928-840-3 | ||
| 020 | |a 9783039288403 | ||
| 020 | |a 9783039288397 | ||
| 024 | 7 | |a 10.3390/books978-3-03928-840-3 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Borge Diez, David |4 auth | |
| 700 | 1 | |a Colmenar Santos, Antonio |4 auth | |
| 700 | 1 | |a Rosales Asensio, Enrique |4 auth | |
| 245 | 1 | 0 | |a District Heating and Cooling Networks |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (270 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 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 | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2263 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/45291 |7 0 |z DOAB: description of the publication |