INTELLIGENT SELF-DEVELOPING AND SELF-ADAPTIVE ELECTRIC LOAD FORECASTER BASED ON TYPE-2 FUZZY BAYESIAN YING-YANG LEARNING ALGORITHM

摘要

In new advocated "smart grid" development, an electric load forecaster should possess high-level intelligence in order to handle higher uncertainly, indefinileness, and variability on electric load demand. The intelligence is referred to as self-learning, self-adaptability, and the highest capability of handling various uncertainties, which the forecaster should possess. In this study, a novel methodology, self-developing and self-adaptive fuzzy neural networks using lype-2 fuzzy Bayesian Ying-Yang learning algorithm (SDSA-FNN-T2BYYL) is proposed. Its novelty is that (1) the Bayesian Ying-Yang learning algorithm (BYYL) is used to construct a compact system structure automatically. (2) Further, a novel T2 fuzzy BYYL is presented, which integrates lype-2 (T2) fuzzy theory and BYYL in order to achieve two objectives simultaneously: compact system structure and belter handling of data uncertainly. (3) Because a training dalasel cannot include all possible operation conditions, the system should be able to restructure continuously for good generalization. Consequently, a T2 fuzzy BYY splil-and-merge algorithm is proposed. The proposed method is validated using a real operational dataset collected from a Macao electric utility. Simulation and lest results reveal that SDSA-FNN-T2BYYL has superior accuracy for load forecasting over other existing relevant techniques.