Abstract
The power grid, which is one of the important infrastructures, has a very challenging issue to stably manage electric power. The Electrical Network Frequency (ENF) which is the supply frequency on the power grid, however, has small variations near a constant frequency over time. In this paper, we studied the feasibility of predicting ENF values to operate the power grid reliably. To forecast ENF values, we analyzed the ENF signals by using auto-correlation and correlation coefficient. Based on the analysis results, we employed two approaches to forecast ENF values using a kernel regression model with correlation coefficient and autoregressive moving average model. To evaluate the accuracy of the proposed prediction algorithm, we experimented ENF data for 29 days in three power grids of the United States; the Eastern, the Western, and the Texas power grid. The results of our suggested methods presented the remarkable performance in forecasting ENF signals.
Original language | English |
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Title of host publication | ICTC 2019 - 10th International Conference on ICT Convergence |
Subtitle of host publication | ICT Convergence Leading the Autonomous Future |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1218-1223 |
Number of pages | 6 |
ISBN (Electronic) | 9781728108926 |
DOIs | |
Publication status | Published - 2019 Oct |
Event | 10th International Conference on Information and Communication Technology Convergence, ICTC 2019 - Jeju Island, Korea, Republic of Duration: 2019 Oct 16 → 2019 Oct 18 |
Publication series
Name | ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future |
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Conference
Conference | 10th International Conference on Information and Communication Technology Convergence, ICTC 2019 |
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Country/Territory | Korea, Republic of |
City | Jeju Island |
Period | 19/10/16 → 19/10/18 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This work was supported under the framework of international cooperation program managed by National Research Foundation of Korea(No.2017K1A3A1A17092614).
Publisher Copyright:
© 2019 IEEE.
Keywords
- Data Mining
- Electric Network Frequency
- Prediction
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Information Systems and Management
- Management of Technology and Innovation
- Safety, Risk, Reliability and Quality
- Media Technology
- Control and Optimization