Tsinghua Science and Technology


load forecaster, Bayesian Ying-Yang learning algorithm, type-2 fuzzy theory


In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning (SDSA-FNN-T2FBYYL) algorithm and multi-objective optimization is proposed. The features of this methodology are as follows: (1) A Bayesian Ying-Yang Learning (BYYL) algorithm is used to construct a compact but high-performance system automatically. (2) A novel multi-objective T2FBYYL is presented that integrates the T2 fuzzy theory with BYYL to automatically construct its best structure and better tackle various data uncertainty problems simultaneously. (3) The weighted sum multi-objective optimization technique with combinations of different weightings is implemented to achieve the best trade-off among multiple objectives in the T2FBYYL. The proposed methods are applied to electric load forecast using a real operational dataset collected from Macao electric utility. The test results reveal that the proposed method is superior to other existing relevant techniques.


Tsinghua University Press