Smart electricity distribution in residential areas: Internet of Things (IoT) based advanced metering infrastructure and cloud analytics
Abstract
Energy is the foundation block for any socio economic development. There is a significant disparity in the growth rate of population fuelling a demand for energy and the installed capacity base of energy generation in India. Many countries worldwide, have adopted smart cities and smart metering program to cope up with the pressure of growing population and limited infrastructure with mixed results. The Indian government smart cities program to develop over hundred cities will require efficient power management to ensure uninterrupted power supply to all house hold and commercial establishments. This would require commissioning of smart meters generating petabytes (PB) of data on current readings, pilferage, damage, over current drawn, power factor and prepaid billing among other parameters of interest. This huge flow of data would require a supporting Information Technology infrastructure hosted on anytime-anywhere and highly scalable cloud platform providing storage and immense computing power. The beneficiary of such infrastructure would be the citizens, distribution companies and its analytics would help the policy makers. This paper aims at identifying the current energy developments in India, identify the use of ICT technology including Cloud and Internet of Things being adopted worldwide and finally the authors suggest key technology and power components for a Residential Smart Energy Distribution and lists down innovative analytics which can be derived from these compo…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Hardware requirements of middleware in the IoT context can be a concern.
“However, hardware requirements of middleware in the IoT context can be a concern [28] [29] [30].”
From Edge Computing and Microservices Middleware for Home Energy Management Systems (2022) · cites this paper as [29]
Cited by
Sorted by citations
The role of big data analytics in Internet of Things
Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.06.013 · Cited by 629 · see citing works
Edge Computing and Microservices Middleware for Home Energy Management Systems
Ferreira, L. C. B. C., Borchardt, A. D. R., Cardoso, G. D. S., Mendes Lemes, D. A., Sousa, G. R. D. R. D., Neto, F. B., de Lima, E. R., Fraidenraich, G., Cardieri, P., & Meloni, L. G. P. (2022) · IEEE Access · doi:10.1109/access.2022.3214229 · Cited by 28 · see citing works
“IoT context can be a concern [28], [29], [30].”
Background
- Authors
- Ramakrishnan, R., & Gaur, L.
- Affiliations
- Amity University (IN)
- ORCID
- Gaur, Loveleen
- Venue
- 2016 International Conference on Internet of Things and Applications (IOTA)
- Publisher
- IEEE
- Published
- 2016-01
- Pages
- 46-51
- OpenAlex
- W2512604976
- Cited by
- 46
- References
- 0
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 5 · 2022: 5 · 2021: 6 · 2020: 6 · 2019: 8 · 2018: 5 · 2017: 6
- FWCI
- 8.91
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electricity Theft Detection Techniques
- Keywords
- residential energy management, Internet of Things, advanced metering infrastructure, smart meters, cloud analytics, smart cities, energy conservation, electricity theft detection, India energy demand, operational cost reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review