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doi: 10.3390/en13020494
Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.
Internet of things, Technology, Energy storage, 330, flexible demand, energy storage, Smart energy systems, T, energy in buildings, Smart grid, IoT applications, internet of things, 004, smart energy systems, Energy efficiency, Flexible demand, iot applications, Energy in buildings, smart grid, energy efficiency
Internet of things, Technology, Energy storage, 330, flexible demand, energy storage, Smart energy systems, T, energy in buildings, Smart grid, IoT applications, internet of things, 004, smart energy systems, Energy efficiency, Flexible demand, iot applications, Energy in buildings, smart grid, energy efficiency
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 532 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 0.1% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 0.01% |