Title :
The Short-Term Load Forecasting by Applying the Fuzzy Neural Net
Author :
Wang Xiao-Wen ; Fu Xuan ; Sun Xiao-Yu ; Wu Zhi-Hong
Author_Institution :
Coll. of Renewable Energy, Shenyang Inst. of Eng., Shenyang, China
Abstract :
The fuzzy system used for short-term load forecasting is put forward. This system, possesses the structure of neural net and learning algorithm, addressed as fuzzy neural net FNN. FNN generates the rules with the existing history loads and supplements the rules with minimum membership method. After the parameters of rule have been amended, the output of FNN can be well coincident with the data of loads. Once being trained, FNN can forecast future loads right away.
Keywords :
fuzzy neural nets; learning (artificial intelligence); load forecasting; power engineering computing; FNN; fuzzy neural net; learning algorithm; minimum membership method; short-term load forecasting; Artificial neural networks; Fuzzy neural networks; Load forecasting; Load modeling; Mathematical model; Artificial neural net; Fuzzy system; Load forecasting;
Conference_Titel :
Intelligent Networks and Intelligent Systems (ICINIS), 2013 6th International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4799-2808-8
DOI :
10.1109/ICINIS.2013.52
