Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
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.
Recommended Citation
Hur, Jin and Ahn, Eunji, "An Enhanced Short-term Forecasting of Wind Generating Resources based on Edge Computing in Jeju Carbon-Free Islands" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 6.
https://aisel.aisnet.org/hicss-57/es/renewable_resources/6
An Enhanced Short-term Forecasting of Wind Generating Resources based on Edge Computing in Jeju Carbon-Free Islands
Hilton Hawaiian Village, Honolulu, Hawaii
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.
https://aisel.aisnet.org/hicss-57/es/renewable_resources/6