Smart Energy Meter: Applications, Bibliometric Reviews and Future Research Directions

被引：3
作者：
Kuralkar S. [1 ]
Mulay P. [1 ]
Chaudhari A. [1 ]
机构：
[1] Symbiosis Institute of Technology (SIT), Symbiosis International, Deemed University, Pune
来源：
Science and Technology Libraries | 2020年 / 39卷 / 02期
关键词：
bibliometric Analysis; demand Response; direct-Indirect charging; load Profiling; machine learning; Smart Energy Meter;
D O I：
10.1080/0194262X.2020.1750081
中图分类号：
学科分类号：
摘要：
Now, Smart Energy Meters (SEMs) have become very popular in controlling and managing electricity consumption in residential, commercial, industrial sectors, to name a few. SEMs collects the fine-grained energy-consumption data at a regular time interval (every hour). Such a low sampling data rate can be useful in research, evaluating the higher consumption of electricity, etc. However, existing research work cannot adequately assess the qualitative, quantitative, and logical properties of the data that could theoretically improve the performance of classification and analytics. The purpose of this bibliometric analysis is to understand the reach of SEMs and its data analysis worldwide and across the various applications to make the concept clear for future researchers. The Scopus, Semantic Scholar and Crossref databases are used for performing the bibliometric analysis. This research study revealed that the insights of the SEMs data-enabled significant advancement in many fields such as energy consumption pattern analysis and prediction, demand response, load profiling, and direct-indirect phone charging analysis. © 2020, © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.
引用
收藏
页码：165 / 188
页数：23
相关论文
共 27 条
  • [1] Aimal S., Javaid N., Rehman A., Ayub N., Sultana T., Tahir A., Data analytics for electricity load and price forecasting in the smart grid, Advances in Intelligent Systems and Computing Springer Nature, 927, (2019)
  • [2] Chaudhari A., Mulay P., A bibliometric survey on incremental clustering algorithm for electricity smart-meter data analysis, Iran Journal of Computer Science, 2, 4, pp. 197-206, (2019)
  • [3] Chaudhari A., Joshi R., Mulay P., Kotecha K., Kulkarni P., Bibliometric survey on incremental clustering algorithms, Library Philosophy and Practice, (2019)
  • [4] Chen Y.Y., Lin Y.H., A smart autonomous time- and frequency-domain analysis current sensor-based power meter prototype developed over fog-cloud analytics for demand-side management, Mdpi Ag, 19, (2019)
  • [5] Devlin M., Hayes B.P., Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data, IEEE Transactions on Consumer Electronics, 65, 3, pp. 339-348, (2019)
  • [6] Dudek G., Gawlak A., Kornatka M., Szkutnik J., Analysis of smart meter data for electricity consumers, Conference:15th International Conference on the European Energy Market (EEM), Łódź, Poland, (2018)
  • [7] Giordano V., Gangale F., Fulli G., Jimenez M.S., Smart Grid projects in Europe: Lessons learned and current developments, Energy and Transport, (2011)
  • [8] Herath P., Fusco V., Navarro M., Venayagamoorthy G.K., Squartini S., Piazza F., Corchado J.M., Computational intelligence based demand response management in a microgrid, IEEE Transactions on Industry Applications, 55, 1, pp. 732-740, (2018)
  • [9] Khan Z., Jayaweera A.D., Smart meter data based load forecasting and demand side management in distribution networks with embedded PV systems, IEEE Access, 8, pp. 2631-2644, (2019)
  • [10] Krishnan S., Chinthakunta V., Kok Swee S., Smart home meter profiler with load authentication, shock protection, fault proof and restricted demand management, International Journal of Technology, 10, 7, pp. 1286-1296, (2019)
123