A monthly electricity consumption forecasting method based on vector error correction model and self-adaptive screening method. (February 2018)

• Record Type: Journal Article
• Authors: Guo, Hongye; Chen, Qixin; Xia, Qing; Kang, Chongqing; Zhang, Xian
• Is Part Of: International journal of electrical power & energy systems. Volume 95(2018)
• Page Start: 427
• Page End: 439
• Publication Date: 2018-02
• Subjects: Electricity consumption forecasting -- External economic factors -- Vector error correction model -- Self-adaptive screening -- Seasonal decomposition; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2017.09.011
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A novel mid-term electricity consumption forecasting method is proposed. The method explores potential impacts between various external economic factors. The method addresses correlations and time lag effects among input factors. A specific self-adaptive screening method is proposed. Abstract: Economic growth has greatly fluctuated around the world in recent years, and external economic factors (EEFs) have imposed more obvious effects on electricity consumption. To improve the accuracy and applicability of mid-term, especially monthly, electricity consumption forecasting, a novel monthly electricity consumption forecasting framework (denoted as SAS-SVECM for short) based on vector error correction model (VECM) and self-adaptive screening (SAS) method is proposed in this paper, which fully explores and integrates the potential impacts from and relationships between EEFs. The SAS-SVECM firstly implements X-12-ARIMA to extract seasonal peaks from the electricity consumption and EEF time series. Second, a VECM is used to address correlations and time lag effects between electricity consumption and EEFs. And a SAS method is proposed to identify the most possible influential EEF self-adaptively, which appropriately addresses the contradiction between data quantity and data length. The SAS-SVECM achieves significant forecasting accuracy enhancement and good adaptability. Finally, an empirical example, using real monthly electricity consumption and macroeconomic data of China (2000–2014), was studied to verify the effectiveness of SAS-SVECM.