An Enhanced Short-term Forecasting of Wind Generating Resources based on Edge Computing in Jeju Carbon-Free Islands
Files
Date
2024-01-03
Authors
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
2926
Ending Page
Alternative Title
Abstract
As the Internet-of-Things makes various types of sensing possible, edge computing has been developed to collect, store, and analyze vast amounts of data. It is becoming a great resource for future industries because unlike in cloud systems, large amounts of data can be processed efficiently and immediately near the source. After comparing the characteristics of cloud computing and edge computing techniques, SCADA systems in various countries were analyzed. Lastly, in this study, we propose an SLA architecture for wind power output forecast which uses data collected from edge computing. To validate the proposed method, we analyzed empirical data obtained from Korea wind farm based on ARIMAX and Monte-Carlo simulations and found that the NMAE (Normalized MAE) value for the forecasting period was about 2%. Therefore, this study focuses on increasing the flexibility of the distribution grid and look forward to deploying this architecture to energy management systems in South Korea.
Description
Keywords
Distributed, Renewable, and Mobile Resources, analog-ensemble model, edge computing, kriging-based spatial modeling, short-term wind power forecasting, statistical learning analysis
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 57th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.