Advanced Metering Infrastructure Data: Overviews for the Big Data Framework

Authors

  • Md. Mostafijur Rahman Lecturer, Business Administration, First Capital University of Bangladesh (FCUB), Chuadanga, BANGLADESH
  • Mahesh Babu Pasupuleti Data Analyst, Department of IT, iMinds Technology Systems, Inc., 1145 Bower Hill Rd, PA, USA
  • Harshini Priya Adusumalli Software Developer, iMinds Technology systems, Inc., 1145 Bower Hill Rd, PA, USA

DOI:

https://doi.org/10.18034/ra.v7i3.602

Keywords:

Big Data, Advanced Metering Infrastructure, AMI Data, Data Analytics, Smart Meter

Abstract

The Advanced Metering Infrastructure (AMI) statistics provide real-time information about power use as well as social, demographic, and economic aspects within a community. This study proposes a Data Analytics/Big Data architecture for leveraging AMI data in Smart City applications. The framework has three main components. First, the architectural view positions AMI within the SGAM. Second, the methodological view describes the DIKW hierarchy and NIST Big Data interoperability model's translation of raw data into knowledge. Finally, human expertise and talents to analyze the results and translate knowledge into wisdom are a connecting aspect between the two approaches. A binding element that supports optimal and efficient decision-making is added to our new perspective. We made a case study to demonstrate our framework. A load forecasting application is implemented in Retail Electricity Provider (REP). Some of the REP's marketplaces have a MAPE of less than 5%. The scenario also highlights the binding element's effect on new development options and as a feedback mechanism for more forceful decision making.

Downloads

Download data is not yet available.

References

Achar, S. (2018a). Data Privacy-Preservation: A Method of Machine Learning. ABC Journal of Advanced Research, 7(2), 123-129. https://doi.org/10.18034/abcjar.v7i2.654

Achar, S. (2018b). Security of Accounting Data in Cloud Computing: A Conceptual Review. Asian Accounting and Auditing Advancement, 9(1), 60–72. https://4ajournal.com/article/view/70

Adusumalli, H. P. (2016a). Digitization in Production: A Timely Opportunity. Engineering International, 4(2), 73-78. https://doi.org/10.18034/ei.v4i2.595

Adusumalli, H. P. (2016b). How Big Data is Driving Digital Transformation?. ABC Journal of Advanced Research, 5(2), 131-138. https://doi.org/10.18034/abcjar.v5i2.616

Adusumalli, H. P. (2017a). Mobile Application Development through Design-based Inves-tigation. International Journal of Reciprocal Symmetry and Physical Sciences, 4, 14–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/58

Adusumalli, H. P. (2017b). Software Application Development to Backing the Legitimacy of Digital Annals: Use of the Diplomatic Archives. ABC Journal of Advanced Re-search, 6(2), 121-126. https://doi.org/10.18034/abcjar.v6i2.618

Adusumalli, H. P. (2018). Digitization in Agriculture: A Timely Challenge for Ecological Perspectives. Asia Pacific Journal of Energy and Environment, 5(2), 97-102. https://doi.org/10.18034/apjee.v5i2.619

Adusumalli, H. P., & Pasupuleti, M. B. (2017). Applications and Practices of Big Data for Development. Asian Business Review, 7(3), 111-116. https://doi.org/10.18034/abr.v7i3.597

Bere¸ S. A., Genge, B., Kiss, I. (2014). A Brief Survey on Smart Grid Data Analysis in the Cloud. In Proceedings of the 8th International Conference on Interdisciplinary in En-gineering—INTER-ENG, Tirgu Mures, Romania, 9–10 October 2014, 858–865.

Borlase, S. (2017). Smart Grids: Advanced Technologies and Solutions; CRC Press: Boca Raton, FL, USA.

Daki, H., El Hannani, A., Aqqal, A., Haidine, A., Dahbi, A. (2017). Big Data management in smart grid: concepts, requirements and implementation. J. Big Data, 4, 1–19.

Erl, T., Khattak, W., Buhler, P. (2016). Big Data Fundamentals: Concepts, Drivers & Tech-niques, 1st ed.; Pearson: Upper Saddle River, NJ, USA.

Fadziso, T., Adusumalli, H. P., & Pasupuleti, M. B. (2018). Cloud of Things and Interwork-ing IoT Platform: Strategy and Execution Overviews. Asian Journal of Applied Sci-ence and Engineering, 7, 85–92. Retrieved from https://upright.pub/index.php/ajase/article/view/63

Liu, X.; Golab, L.; Golab, W.; Ilyas, I.F.; Jin, S. (2017). Smart Meter Data Analytics. ACM Trans. Database Syst, 42, 1–39.

Pasupuleti, M. B. (2015). Problems from the Past, Problems from the Future, and Data Sci-ence Solutions. ABC Journal of Advanced Research, 4(2), 153-160. https://doi.org/10.18034/abcjar.v4i2.614

Pasupuleti, M. B. (2016a). Data Scientist Careers: Applied Orientation for the Begin-ners. Global Disclosure of Economics and Business, 5(2), 125-132. https://doi.org/10.18034/gdeb.v5i2.617

Pasupuleti, M. B. (2016b). The Use of Big Data Analytics in Medical Applica-tions. Malaysian Journal of Medical and Biological Research, 3(2), 111-116. https://doi.org/10.18034/mjmbr.v3i2.615

Pasupuleti, M. B. (2017). AMI Data for Decision Makers and the Use of Data Analytics Ap-proach. Asia Pacific Journal of Energy and Environment, 4(2), 65-70. https://doi.org/10.18034/apjee.v4i2.623

Pasupuleti, M. B., & Adusumalli, H. P. (2018). Digital Transformation of the High-Technology Manufacturing: An Overview of Main Blockades. American Journal of Trade and Policy, 5(3), 139-142. https://doi.org/10.18034/ajtp.v5i3.599

Pasupuleti, M. B., & Amin, R. (2018). Word Embedding with ConvNet-Bi Directional LSTM Techniques: A Review of Related Literature. International Journal of Recip-rocal Symmetry and Physical Sciences, 5, 9–13. Retrieved from https://upright.pub/index.php/ijrsps/article/view/64

Rowley, J. (2017). The wisdom hierarchy: Representations of the DIKW hierarchy. J. Inf. Sci. 2007, 33, 163–180.

Shyam, R., Bharathi Ganesh, H. B., Kumar, S. S., Poornach, P., Soman, K. P. (2015). Apache Spark a Big Data Analytics Platform for Smart Grid. Procedia Technol., 21, 171–178.

Stoyanov, S., and Kakanakov, N. (2017). Big data analytics in electricity distribution sys-tems. In Proceedings of the 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 22–26 May 2017, 205–208.

Uslar, M., Specht, M., Dänekas, C., Trefke, J., Rohjans, S., González, J. M., Rosinger, C., Bleiker, R. (2013) Standardization in Smart Grids—Introduction to IT-Related Meth-odologies, Architectures and Standards; Power Systems; Springer Science & Business Media: Berlin/Heidelberg, Germany, p. 300.

--0--

Downloads

Published

2019-12-20

How to Cite

Rahman, M. M., Pasupuleti, M. B., & Adusumalli, H. P. (2019). Advanced Metering Infrastructure Data: Overviews for the Big Data Framework . ABC Research Alert, 7(3), 159–168. https://doi.org/10.18034/ra.v7i3.602

Issue

Vol. 7 No. 3 (2019): September-December Issue

Section

Review Paper

License

ABC Research Alert is an Open Access journal. Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a CC BY-NC 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of their work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal. We require authors to inform us of any instances of re-publication.