Classification of daily load characteristics curve and forecasting of short-term load based on fuzzy clustering and improved BP algorithm

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control

Volumn 40, Issue 3, 2012, Pages 56-60

  Li, Zuo ^a   Zhou, Bu Xiang ^a   Lin, Nan ^b  

^a SICHUAN UNIVERSITY   (China)

^b Sichuan Electric Vocational and Technical College   (China)

Author keywords

Additional momentum; BP neural; Daily load characteristics curve; Fuzzy clustering; Short term load; Variable learning rate

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BP ALGORITHM; BP NEURAL; CLUSTERING TECHNOLOGIES; CURVE METHOD; DAILY LOAD CURVES; FORECASTING PRECISION; IMPROVED BP ALGORITHMS; LOAD CHARACTERISTICS; LOAD CURVES; NEURAL MODELS; SHORT TERM LOAD FORECASTING; SHORT-TERM LOAD; TYPICAL CURVES; VARIABLE LEARNING RATE;

BACKPROPAGATION ALGORITHMS; FUZZY CLUSTERING; LEARNING ALGORITHMS; NEURAL NETWORKS;

FORECASTING;

EID: 84863265138     PISSN: 16743415     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (14)

1. LIAO Ni-huan, HU Zhi-hong, MA Ying-ying, et al. Review of the short-term load forecasting methods of electric power system [J]. Power System Protection and Control, 2011, 39(1): 147-148.
2. KANG Chong-qing, XIA Qing, LIU Mei. Power system load forecasting [M]. Beijing: China Electric Power Press, 2007.
3. WEI Jun, ZHOU Bu-xiang, LIN Nan. Short-term load forecasting based on MG-CACO and SVM method [J]. Power System Protection and Control, 2009, 37(4): 36-37.
4. LIU Yao-nian, FU Xiang-yun, ZHANG Wen-sheng, et al. Short-term load forecasting method based on fuzzy pattern recognition and fuzzy cluster theory [J]. Transaction of China Electrotechnical Society, 2002, 17(5): 83-86.
5. CHEN You, WANG Xian, LI Yu-zeng. Electricity price forecasting based on transfer function models for period-decoupled time series [J]. Power System Protection and Control, 2008, 36(16): 1-4.
6. SHI Lin-jun, TANG Guo-qing, MA Xiao-dong. Development of a power system mid-long term load forecasting software based on object oriented programming [J]. Power System Protection and Control, 2007, 35(14): 59-62.
7. CHENG Hao-zhong. Power system planning [M]. Beijing: China Electric Power Press, 2008.
8. Gavrilas M, Ciutea I, Tanasa C. Short-term load forecasting with artificial neural network models [J]. Proceedings of IEE CIRED, Netherlands, 2001, 25(12): 28-31.
9. ZHONG Wei-kuan. The application of fuzzy clustering algorithm in analysis of customer load curve [J]. East China Electric Power, 2007, 32(8): 97-100.
10. CHEN Rou-yi, ZHANG Yao, WU Zhi-gang, et al. Application of improving fuzzy clustering algorithm to power load forecasting [J]. Proceedings of the CSU-EPSA, 2005, 17(3): 73-77.
11. WANG Chang-fei. Study and realization of short-term load forecasting in power system [D]. Zhengzhou: Zhengzhou University, 2005.
12. Fabrizio A. Fast nearest neighbor condensation for large data sets classification [J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(11): 1450-1464.
13. Hagan M T, Demuth H B, Beale M H. Neural network design [M]. Beijing: China Machine Press, 2002.
14. FENG Ying-jun, ZHANG Shao-zhong. Analysis on paralysis of BP networks [J]. Journal of Harbin Institute of Technology, 2000, 32(5): 81-83.