A Cloud-Based System Architecture for Advanced Metering in Smart Cities

Pires, Felipe Marques; Quiñonez, Lorena León; Mendes, Leonardo de Souza

doi:10.1109/iemcon.2019.8936283

Cited by 10 publications

(1 citation statement)

References 25 publications

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“…RELATED WORK Related work stems from the contributions of papers that propose architectures for energy data collection. Pires et al [35] propose an architecture for enabling cloud services to use energy data from smart meters in the context of smart cities. In this architecture, smart meters are connected to a custom gateway (e.g., a Raspberry Pi computer) that is configured to send data to services via the Internet.…”

Section: E Power Grid Operationmentioning

confidence: 99%

A Framework for Enabling Cloud Services to Leverage Energy Data

Karagiannis,

Kashyap,

Zechner

et al. 2023

2023 IEEE International Conference on Cloud Engineering (IC2E)

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Cloud services have been well integrated into various sectors of contemporary societies such as transportation, healthcare, and work. However, the energy sector has made little progress in offering cloud services to energy consumers. In some cases, even basic features (such as energy consumption monitoring and visualization of households) are still unattainable. To foster innovation in cloud-based energy services, governments implement regulations that moderate access to energy data via the energy providers' platforms. Despite these regulations, cloud service providers may still struggle to aggregate energy data because each energy provider may be using different procedures, authentication mechanisms, and data semantics. Consequently, aggregating energy data at scale becomes challenging, as it requires achieving compatibility with diverse platforms. To address this challenge, we propose a cloud-based framework that handles all interactions with energy providers and prepares the data for further processing by services. Additionally, we analyze the socioeconomic impact of our approach, and we outline novel use cases that may emerge due to the proposed framework.

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Section: E Power Grid Operationmentioning

confidence: 99%