A Machine Learning implementation on Internet of Things Smart Meter Operations

Authors(2) :-Srisudha Garugu, Srisatya Kalyani Anala

An Internet of Things’ (IoT) attached network and structures reshow an incredible archetype shuffle. We suggest a scheme to get a compromise-support structure (DSS) a well known operates inside the IoT reorganization. The DSS leverages progressed data of electrical resourceful feet (ESM) chain communication-quality testimony to recuperate lose forecasting’s for quick swing field operations and supply tribal resolution recommendations relating to even if to issue a scholar to a patron whereabouts to get to the bottom of an ESM send. The style is temporarily evaluated the use of input sets originating at a monetary net. We teach the competence of our manner having a finish Bayesian Network forecast style and relate with triple neural networks prophecy variety classifiers: Naïve Bayes, Random Forest and Decision Tree. Results testify to who our method generates statistically memorable estimations and which the DSS determination get better the price competence of ESM web operations and maintenance.

Authors and Affiliations

Srisudha Garugu
Assistant Professor Pydah College of Engineering And Technaology, Gambheeram,Visakhaptnam, Andhra Pradesh, India
Srisatya Kalyani Anala
Assistant Professor Pydah College of Engineering And Technaology, Gambheeram,Visakhaptnam, Andhra Pradesh, India

Analytics, Bayesian Networks, Cyber-Physical Systems (CPS), Decision Support System, Information and Communication Technologies (ICT), Internet of Things (IoT), Machine Learning, Machine-to-Machine (M2M), Operations and Maintenance, Smart Cities, Smart Grid, Smart Meters, Utility.

  1. Ericsson. (2015). 5G Systems Enabling Industry & Society Transformation, Ericsson White Paper. Stockholm.
  2. Haben, S., Singleton, C., & Grindrod, P. (2016). Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data. IEEE Transactions on Smart Grid Journal, 136-144.
  3. Hussain, G. A., Kumpulainen, L., Kluss, J. V., Lehtonen, M., & Kay, J. A. (2013). The Smart Solution for the Prediction of Slowly Developing Electrical Faults in MV Switchgear Using Partial Discharge Measurements. IEEE Transactions on Power Delivery Journal, 2309-2316.
  4. Luan, W., Peng, J., Maras, M., Lo, J., & Harapnuk, B. (2015). Smart Meter Data Analytics for Distribution Network Connectivity Verification. IEEE Transactions on Smart Grid Journal, 1964-1971.
  5. Krishnan, A. (2015, December 8). Smart Meter Rollouts from Water Utilities Gain Momentum as Total Installed Smart Meters Swarm to Reach 1.1 Billion Units by 2021. Retrieved 2016, from ABIresearch: https://www.abiresearch.com/press/smart-meter-rollouts-water-utilities-gain-momentum/
  6. Siryani, J., Mazzuchi, T., & Sarkani, S. (2015). Framework using Bayesian Belief Networks for Utility Effective Management and Operations. 2015 IEEE First International Conference on Big Data Computing Service and Applications (p. 7). San Francisco Bay, CA: IEEE.
  7. Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An Information Framework for Creating a Smart City Through Internet of Things. IEEE Internet of Things Journal, 112-121.
  8. Smaragdakis, G., Laoutaris, N., Oikonomou, K., Stavrakakis, I., & Bestavros, A. (2014). Distributed Server Migration for Scalable Internet Services Deployment. IEEE/ACM Transactions on Networking, 917-930.
  9. Stankovic, J. A. (2014). Research Directions for the Internet of Things. IEEE Internet of Things Journal, 3-9.
  10. Fahrion, M. (2014). Evolving from SCADA to IoT. Quatech .
  11. ITIL. (2011). ITIL Service Operation Processes.
  12. Lin, Y.-B., Lin, Y.-W., Chih, C.-Y., Li, T.-Y., Tai, C.-C., Wang, Y.-C., et al. (2015). EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design amd Art. IEEE Internet of Things Journal, 551-561.
  13. Brown, E. A. (2016, July 28). NIST's Network-of-Things Model Builds Foundation to Help Define the Internet of Things. Retrieved 2016, from https://www.nist.gov/:https://www.nist.gov/news-events/news/2016/07/nists-network-things-model-builds-foundation-help-define-internet-things.
  14. Darwiche, A. (2009). Modeling and Reasoning with Bayesian networks. Cambridge University Press.
  15. Mainetti, L., Mighali, V., & Patrono, L. (2015). A Software Architecture Enabling the Web of Things. IEEE Internet of Things Journal, 445-454.
  16. Pai, G. J., & Dugan, J. B. (2007). Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 675-686.
  18. Eveleigh, T., Wandji, K. T., Sarkani, S., Holzer, T. H., & Keiller, P. A. (2013). Comparative analysis of Bayesian and classical approaches for software reliability measurement. Washington DC., DC, US.
  19. Siddharthan, A., Lambin, C., Robinson, A.-M., Sharma, N., Comont, R., O'mahony, E., et al. (2016). Crowdsourcing Without a Crowd: Reliable Online Species Identification Using Bayesian Models to Minimize Crowd Size. ACM Transactions on Intelligent Systems and Technology, 1-20.
  20. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 22-32.
  21. Pan, J., Jain, R., Paul, S., Vu, T., Saifullah, A., & Sha, M. (2015). An Internet of Things Framework for Smart Energy Buildings: Designs, Prototype, and Experiments. IEEE Internet of Things Journal, 527-537.
  22. Cai, B., Liu, Y., & Xie, M. (2017). A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 276-285.
  23. Liu, K., Xi, Z., & Shi, J. (2014). Adaptive Sensor Allocation Strategy for Process Monitoring and Diagnosis in a Bayesian Network. IEEE 452-462.
  24. Suryachandra, P., & Reddy, V. S. (2016). Comparison of machine learning algorithms for breast cancer. 2016 International Conference on Inventive Computation Technologies (ICICT) (pp. 1-6). IEEE Conference Publications.
  25. Misirli, A. T., & Bener, A. B. (2014). Bayesian Networks For Evidence-Based Decision-Making in Software Engineering. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 533-554.
  26. Li, Z., & Oechtering, T. J. (2015). Privacy-Aware Distributed Bayesian Detection. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 1345-1357.

Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 593-599
Manuscript Number : CSEIT1831126
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Srisudha Garugu, Srisatya Kalyani Anala, "A Machine Learning implementation on Internet of Things Smart Meter Operations", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.593-599, January-February-2018.
Journal URL : http://ijsrcseit.com/CSEIT1831126

Article Preview

Follow Us

Contact Us