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http://dx.doi.org/10.6109/jkiice.2021.25.8.1005

Development of Data Visualized Web System for Virtual Power Forecasting based on Open Sources based Location Services using Deep Learning  

Lee, JeongHwi (Department of Computer Science, Sangmyung University)
Kim, Dong Keun (Department of Human-Centered Artificial Intelligence, Sangmyung University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.8, 2021 , pp. 1005-1012 More about this Journal
Abstract
Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.
Keywords
Power demand monitoring; Power demand prediction; Deep learning; Location information; Data visualization;
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