@inproceedings{2457260d36b54c79a1615be339e83137,
title = "Demand power forecasting with data mining method in smart grid",
abstract = "Nowadays increasing electricity demand is a key issue. As the demand is increasing day by day, obtaining energy efficiency is also getting important. Hence developing accurate demand forecasting methods is crucial for ensuring energy efficiency through efficient system operation. In this paper, we suggested a demand forecasting method with data mining techniques. We proposed a hybrid method which combined K-means clustering, Bayesian classification and ARIMA. Most of the previous research tried to solve this issue from supply side management but here in this paper the proposed forecasting model works on consumer side. Case study has been carried out with actual load profile from Jeju island, South Korea. The minimum error rate is 0.1853 from proposed Hybrid Model. The performance of the proposed model was also compared with the Neural Network based forecasting. The comparison shows better performance of proposed model compared to Neural Network.",
keywords = "Classification algorithms, Demand forecasting, Neural networks, Pattern clustering, Smart grids, Time series analysis",
author = "Seunghyeon Park and Sekyung Han and Yeongik Son",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2017 ; Conference date: 04-12-2017 Through 07-12-2017",
year = "2018",
month = jun,
day = "8",
doi = "10.1109/ISGT-Asia.2017.8378423",
language = "English",
series = "2017 IEEE Innovative Smart Grid Technologies - Asia: Smart Grid for Smart Community, ISGT-Asia 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2017 IEEE Innovative Smart Grid Technologies - Asia",
address = "United States",
}
