District Heating and Cooling Networks
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, 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.
CFD model, low-temperature district heating, optimal control, big data frameworks, data mining algorithms, heat pumps, nZEB, energy efficiency, energy consumption forecast, greenhouse gas emissions, Computational Fluid Dynamics, neural networks, Gulf Cooperation Council, TRNSYS, machine learning, parameter analysis, data streams analysis, residential, district heating (DH) network, biomass district heating for rural locations, district cooling, TA1-2040, optimization, hot climate, retrofit, heat reuse, primary energy use, energy prediction, data center, baseline model, hydronic pavement system, thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology, T1-995, domestic, sustainable energy, thermally activated cooling, district heating, energy management in renovated building, prediction algorithm, 4th generation district heating, low temperature district heating system, biomass, air-conditioning, CO2 emissions abatement, time delay, thermal inertia, variable-temperature district heating, low temperature networks, ultralow-temperature district heating, energy system modeling, thermal-hydraulic performance, Scotland, twin-pipe, verification, space cooling
CFD model, low-temperature district heating, optimal control, big data frameworks, data mining algorithms, heat pumps, nZEB, energy efficiency, energy consumption forecast, greenhouse gas emissions, Computational Fluid Dynamics, neural networks, Gulf Cooperation Council, TRNSYS, machine learning, parameter analysis, data streams analysis, residential, district heating (DH) network, biomass district heating for rural locations, district cooling, TA1-2040, optimization, hot climate, retrofit, heat reuse, primary energy use, energy prediction, data center, baseline model, hydronic pavement system, thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology, T1-995, domestic, sustainable energy, thermally activated cooling, district heating, energy management in renovated building, prediction algorithm, 4th generation district heating, low temperature district heating system, biomass, air-conditioning, CO2 emissions abatement, time delay, thermal inertia, variable-temperature district heating, low temperature networks, ultralow-temperature district heating, energy system modeling, thermal-hydraulic performance, Scotland, twin-pipe, verification, space cooling
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