期限(时间)
智能电网
时间序列
特征选择
计算机科学
公制(单位)
梯度升压
滞后
数据挖掘
系列(地层学)
人工智能
机器学习
随机森林
物理
生物
量子力学
计算机网络
古生物学
运营管理
经济
生态学
作者
Raza Abid Abbasi,Nadeem Javaid,Muhammad Nauman Javid Ghuman,Zahoor Ali Khan,Shujat ur Rehman,. Amanullah
出处
期刊:Advances in intelligent systems and computing
日期:2019-01-01
卷期号:: 1120-1131
被引量:33
标识
DOI:10.1007/978-3-030-15035-8_108
摘要
For efficient use of smart grid, exact prediction about the in-future coming load is of great importance to the utility. In this proposed scheme initially we converted daily Australian energy market operator load data to weekly data time series. Furthermore, we used eXtreme Gradient Boosting (XGBoost) for extracting features from the data. After feature selection we used XGBoost for the purpose of forecasting the electricity load for single time lag. XGBoost perform extremely well for time series prediction with efficient computing time and memmory resources usage. Our proposed scheme outperformed other schemes for mean average percentage error metric.
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