Demand power forecasting with data mining method in smart grid
Demand power forecasting with data mining method in smart grid
复制标题
智能电网中数据挖掘方法的需量预测
DOI：
10.1109/isgt-asia.2017.8378423
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Yeon
中科院分区：
文献类型：
--
作者：
Seunghyeon Park;Sekyung Han;Yeon
如今，电力需求的增长是一个关键问题。随着需求日益增加，获得能源效率也变得越来越重要。因此，开发准确的需求预测方法对于通过高效的系统运行确保能源效率至关重要。在本文中，我们提出了一种利用数据挖掘技术的需求预测方法。我们提出了一种结合 K 均值聚类、贝叶斯分类和 ARIMA 的混合方法。以前的大多数研究都试图从供应方管理来解决这个问题，但本文提出的预测模型适用于消费者方。我们对韩国济州岛的实际负荷曲线进行了案例研究。所提出的混合模型的最小错误率为 0.1853。所提出模型的性能也与基于神经网络的预测进行了比较。比较表明，与神经网络相比，所提出的模型具有更好的性能。
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.