期刊
ENERGY
卷 115, 期 -, 页码 734-745出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2016.09.065
关键词
Support vector regression; Fruit fly optimization algorithm; Electricity consumption forecasting; Seasonal mechanism
资金
- Financial Experimental Project of Chongqing University [2013JGSYJX005]
Accurate monthly electricity consumption forecasting can provide the reliable guidance for better energy planning and administration. However, it has been found that the monthly electricity consumption demonstrates a complex nonlinear characteristic and an obvious seasonal tendency. Support vector regression has been widely applied to handle nonlinear time series prediction, but it suffers from the key parameters selection and the influence of seasonal tendency. This paper proposes a novel approach, which hybridizes support vector regression model with fruit fly optimization algorithm and the seasonal index adjustment to forecast monthly electricity consumption. Besides, in order to comprehensively evaluate the forecasting performance of the hybrid model, a small sample of monthly electricity consumption of China and a large sample of monthly electricity retail sales of the United States were employed to demonstrate the forecasting performance. The results show that the proposed hybrid approach is a viable option for the electricity consumption forecasting applications. (C) 2016 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据