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J. W. Taylor and R. Buizza, “Neural Networks Load Forecasting with Whether Ensemble Predictions,” IEEE Transactions on Power Systems, Vol. 17, No. 3, 2002, pp. 626-630. doi:10.1109/TPWRS.2002.800906

has been cited by the following article:

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

  AUTHORS: Xiaojuan Liu, Enjian Bai, Jian’an Fang

  KEYWORDS: Load Forecasting; Fuzzy Time-Series; Weighted; Slide

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.4, November 28, 2012

  ABSTRACT: 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.