- Volume 9 Issue 4
As the fourth industrial revolution, in which people, objects, and information are connected as one, various fields such as smart energy, smart cities, artificial intelligence, the Internet of Things, unmanned cars, and robot industries are becoming the mainstream, drawing attention to big data. Among them, Smart Grid is a technology that maximizes energy efficiency by converging information and communication technologies into the power grid to establish a smart grid that can know electricity usage, supply volume, and power line conditions. Smart meters are equient that monitors and communicates power usage. We start with the goal of building a virtual smart grid and constructing a virtual environment in which real-time data is generated to accommodate large volumes of data that are small in capacity but regularly generated. A major role is given in creating a software/hardware architecture deployment environment suitable for the system for test operations. It is necessary to identify the advantages and disadvantages of the software according to the characteristics of the collected data and select sub-projects suitable for the purpose. The collected data was collected/loaded/processed/analyzed by the Hadoop ecosystem-based big data platform, and used to predict power demand through deep learning.
- Chan-Ho Moon, Bo-Sung Kwon, Dong-Jin Bae, Kyung-Bin Song. "Load Forecasting Algorithm on Weekdays Using Solar Radiation Weight." Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 34.6 (2020): 40-47. DOI : 10.5207/JIEIE.2020.34.6.040 https://doi.org/10.5207/JIEIE.2020.34.6.040
- Dohyun Kim, Ho Jin Jo, Myung Su Kim, Jae Hyung Roh, Jong-Bae Park. "Short-Term Load Forecasting Based on Deep Learning Model." The transactions of The Korean Institute of Electrical Engineers 68.9 (2019): 1094-1099. DOI : 10.5370/KIEE.2019.68.9.1094 https://doi.org/10.5370/KIEE.2019.68.9.1094
- Chi-Yeon Kim, Chae-Rin Kim, Dong-Keun Kim, Hyeong-Jin Choi, Si-Sam Park, Soo-Hwan Cho. "Scaled RMSE and Shewhart Control Chart-based Abnormal Reference Day Detection Method to Improve the Forecasting Accuracy of Community-level Power Demand." The transactions of The Korean Institute of Electrical Engineers 69.2 (2020): 245-257. DOI : 10.5370/KIEE.2020.69.2.245 https://doi.org/10.5370/KIEE.2020.69.2.245
- Jo, Hyunsoo Lee. "Electricity Demand Forecasting Framework using Modified Attention-based LSTM." Journal of Korean Institute of Intelligent Systems 30.3 (2020): 242-250. DOI : 10.5391/JKIIS.2020.30.3.242 https://doi.org/10.5391/JKIIS.2020.30.3.242
- Dong-Ha Shin, Chang-Bok Kim. "A Study on Deep Learning Input Pattern for Summer Power Demand Prediction." The Journal of Korean Institute of Information Technology 14.11 (2016): 127-134. DOI : 10.14801/jkiit.2016.14.11.127 https://doi.org/10.14801/jkiit.2016.14.11.127
- Ji-Won Lee, Hyung-Jun Kim, Mun-Kyeom Kim. "Design of Short-Term Load Forecasting based on ANN Using Bigdata." The transactions of The Korean Institute of Electrical Engineers 69.6 (2020): 792-799. DOI : 10.5370/KIEE.2020.69.6.792 https://doi.org/10.5370/KIEE.2020.69.6.792