Weighted Time-Variant Slide Fuzzy Time-Series Models for Short-Term Load Forecasting

作     者：Xiaojuan Liu Enjian Bai Jian’an Fang 

作者机构：College of Information Science & Technology Donghua University Shanghai China 

出 版 物：《Journal of Intelligent Learning Systems and Applications》 (智能学习系统与应用（英文）)

年 卷 期：2012年第4卷第4期

页      面：285-290页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Load Forecasting Fuzzy Time-Series Weighted Slide 

摘      要：Short-term load forecast plays an important role in the day-to-day operation and scheduling of generating units. Season and temperature are the most important factors that affect the load change, but random factors such as big sport events or popular TV shows can change demand consumption in particular hours, which will lead to sudden load changes. A weighted time-variant slide fuzzy time-series model (WTVS) for short-term load forecasting is proposed to improve forecasting accuracy. The WTVS model is divided into three parts, including the data preprocessing, the trend training and the load forecasting. In the data preprocessing phase, the impact of random factors will be weakened by smoothing the historical data. In the trend training and load forecasting phase, the seasonal factor and the weighted historical data are introduced into the Time-variant Slide Fuzzy Time-series Models (TVS) for short-term load forecasting. The WTVS model is tested on the load of the National Electric Power Company in Jordan. Results show that the proposed WTVS model achieves a significant improvement in load forecasting accuracy as compared to TVS models.