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A smart city is a modern technology-driven urban area which uses sensing devices, information, and communication technology connected to the internet of things (IoTs) for the optimum and efficient utilization of infrastructures and services with the goal of improving the living conditions of citizens. Increasing populations, lower budgets, limited resources, and compatibility of the upgraded technologies are some of the few problems affecting the implementation of smart cities. Hence, there is continuous advancement regarding technologies for the implementation of smart cities. The aim of this Special Issue is to report on the design and development of integrated/smart sensors, a universal interfacing platform, along with the IoT framework, extending it to next-generation communication networks for monitoring parameters of interest with the goal of achieving smart cities. The proposed universal interfacing platform with the IoT framework will solve many challenging issues and significantly boost the growth of IoT-related applications, not just in the environmental monitoring domain but in the other key areas, such as smart home, assistive technology for the elderly care, smart city with smart waste management, smart E-metering, smart water supply, intelligent traffic control, smart grid, remote healthcare applications, etc., signifying benefits for all countries.
n/a --- data mining algorithms --- pressure sensors --- proactive content delivery --- Elman neural network --- cockroaches --- capacitive sensor --- renewable energy --- indoor comfort --- impedance measurement --- Internet of things (IoT) --- context awareness --- redundant capacity --- city behavior --- secondary traffic --- SDN --- ontology --- bi-reflector solar PV system (BRPVS) --- air quality --- ontology development --- assistive living --- sol-gel technique --- decision support system --- ambient assisted living --- LCC converter --- insect surveillance --- sensitivity --- wireless sensor node (WSN) --- unpowered --- load balancing --- wireless sensor network --- dynamic range --- solar --- anomaly detection --- location-based social networks --- real-time assessment --- porous alumina --- IoT --- building integrated photovoltaics (BIPV) --- carbon nanotubes --- six-port structure --- domestic environment reconfiguration --- half bridge --- smart mat --- cloud computing --- differentiated services --- reflection-based --- nanocomposite sensor --- ppm --- chemical sensors --- sensor systems and applications --- tensile testing --- WSN --- smart traps --- ontology-based application --- hotel room comfort
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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 heating --- 4th generation district heating --- data mining algorithms --- energy system modeling --- neural networks --- baseline model --- hydronic pavement system --- biomass district heating for rural locations --- CO2 emissions abatement --- low temperature networks --- ultralow-temperature district heating --- domestic --- optimization --- energy efficiency --- sustainable energy --- big data frameworks --- verification --- energy prediction --- parameter analysis --- greenhouse gas emissions --- time delay --- heat pumps --- primary energy use --- retrofit --- energy consumption forecast --- district heating (DH) network --- low-temperature district heating --- thermal inertia --- variable-temperature district heating --- data streams analysis --- Computational Fluid Dynamics --- energy management in renovated building --- Scotland --- heat reuse --- thermally activated cooling --- district cooling --- space cooling --- Gulf Cooperation Council --- biomass --- TRNSYS --- hot climate --- optimal control --- air-conditioning --- machine learning --- low temperature district heating system --- data center --- twin-pipe --- residential --- prediction algorithm --- CFD model --- nZEB --- thermal-hydraulic performance
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