Title :
CRPSO-based automatic TSK fuzzy model extraction for one hour ahead load forecasting
Author :
Jungwon Yu ; Hansoo Lee ; Yeongsang Jeong ; Eun Kyeong Kim ; Sungshin Kim
Author_Institution :
Dept. of Electr. & Comput. Eng., Pusan Nat. Univ., Busan, South Korea
Abstract :
Electric load forecasting is essential for effective power system planning and operation because many decisions related to power system planning and operation depends on future behavior of electric loads. In this paper, we present automatic TSK fuzzy model extraction method for one hour ahead load forecasting. To extract TSK fuzzy model automatically, cooperative random learning particle swarm optimization (CRPSO) proposed by Zhao et al. is used. Structure and parameter of fuzzy model can be simultaneously extracted by CRPSO. To confirm the effectiveness of CRPSO-based method, two kinds of real world hourly load dataset are used and the performance of the CRPSO-based fuzzy model is compared with ANN and SVR. The experimental results show that CRPSObased TSK fuzzy model outperforms other methods.
Keywords :
cooperative systems; fuzzy set theory; learning (artificial intelligence); load forecasting; particle swarm optimisation; power engineering computing; power system planning; random processes; CRPSO-based automatic TSK fuzzy model extraction; cooperative random learning particle swarm optimization; one hour ahead electric load forecasting; power system operation; power system planning; Artificial neural networks; Computational modeling; Load forecasting; Load modeling; Particle swarm optimization; Predictive models; Springs; Cooperative random learning particle swarm optimization (CRPSO); One hour ahead load forecasting; TSK fuzzy model;
Conference_Titel :
Fuzzy Theory and Its Applications (iFUZZY), 2014 International Conference on
Print_ISBN :
978-1-4799-4590-0
DOI :
10.1109/iFUZZY.2014.7091249
