Title :
A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home
Author :
Ling, S.H. ; Lam, H.K. ; Leung, F.H.F. ; Tam, P.K.S.
Author_Institution :
Centre for Multimedia Signal Process., Hong Kong Polytech. Univ., China
fDate :
6/23/1905 12:00:00 AM
Abstract :
In this paper, a short-term home daily load forecasting system realized by a neural fuzzy network (NFN) and an improved genetic algorithm (GA) is proposed. It can forecast the daily load accurately with respect to different day types and weather information. It is also shown that the improved GA performs better than a traditional GA on some benchmark test functions. By introducing switches in the links of the NFN, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the NFN can be generated automatically. Simulation results for a short-term daily load forecast in an intelligent home are given
Keywords :
fuzzy neural nets; genetic algorithms; home automation; load forecasting; neural net architecture; power system simulation; software performance evaluation; algorithm performance; benchmark test functions; day types; fuzzy membership functions; genetic algorithm; intelligent home; link switches; neural fuzzy network; optimal network structure; optimal rule number; short-term home daily load forecasting system; simulation; weather information; Benchmark testing; Competitive intelligence; Fuzzy neural networks; Genetic algorithms; Intelligent networks; Load forecasting; Performance evaluation; Switches; Telecommunication network reliability; Weather forecasting;
Conference_Titel :
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Conference_Location :
Melbourne, Vic.
Print_ISBN :
0-7803-7293-X
DOI :
10.1109/FUZZ.2001.1008935
