A Method for Load Forecasting Based on Correlated Fuzzy Neural Network and Improved Artificial Bee Colony Algorithm

doi:10.11930/j.issn.1004-9649.20160252

Abstract

Abstract: To improve the accuracy of load forecasting, a load forecasting model is proposed by using correlated fuzzy neural network (CFNN) with consideration of the correlation between the historical load data. An improved artificial bee colony (ABC) algorithm is applied for the parameter identification of the model to reduce the number of fuzzy rules and decrease the complexity of the model. The model is applied to actual load forecasting, and the results show that this model has higher prediction accuracy.

Key words: power systems, load forecasting, CFNN, an improved ABC algorithm, historical load data

CLC Number:

TM715

ZHAO Zhipu, GAO Chao, SHEN Yanxia, CHEN Jie. A Method for Load Forecasting Based on Correlated Fuzzy Neural Network and Improved Artificial Bee Colony Algorithm[J]. Electric Power, 2018, 51(2): 54-60.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.20160252

https://www.electricpower.com.cn/EN/Y2018/V51/I2/54

References

[1] 刘振亚. 中国电力与能源[M]. 北京: 中国电力出版社, 2012.
[2] 廖旎焕, 胡智宏, 马莹莹, 等. 电力系统短期负荷预测方法综述[J]. 电力系统保护与控制, 2011, 39(1): 147-152.
LIAO Nihuan, HU Zhihong, MA Yingying, et al. Review of the short-term load forecasting methods of electric power system[J]. Power System Protection and Control, 2011, 39(1): 147-152.
[3] 袁铁江, 袁建党, 晁勤, 等. 电力系统中长期负荷预测综合模型研究[J]. 电力系统保护与控制, 2012, 40(14): 143-146.
YUAN Tiejiang, YUAN Jiandang, CHAO Qin, et al. Study on the comprehensive model of mid-long term load forecasting[J]. Power System Protection and Control, 2012, 40(14): 143-146.
[4] 丁剑, 艾明建, 吴安平. 西部大开发第二个十年四川省电力需求预测[J]. 中国电力, 2012, 45 (9): 35-39.
DING Jian, AI Mingjian, WU Anping. Electric power demand forecast for sichuan province in the second decade of China's western development program[J]. Electric Power, 2012, 45(9): 35-39.
[5] 温锦斌, 王昕, 李立学, 等. 基于频域分解的短期风电负荷预测[J]. 电工技术学报, 2013, 28(5): 66-72.
WEN Jinbin, WANG Xin, LI Lixue, et al. Short-term wind power load forecasting based on frequency domain decomposition[J]. Transactions of China Electrotechnical Society, 2013, 28(5): 66-72.
[6] 方仍存, 周建中, 彭兵, 等. 电力负荷混沌动力特性及其短期预测[J]. 电网技术, 2008, 32(4): 61-66.
FANG Rengcun, ZHOU Jianzhong, PENG Bing, et al. Chaotic dynamics of power load and its short-term forecasting[J]. Power System Technology, 2008, 32(4): 61-66.
[7] 李霄, 王昕, 郑益慧, 等. 基于改进最小二乘支持向量机和预测误差校正的短期风电负荷预测[J]. 电力系统保护与控制, 2015, 43(11): 63-69.
LI Xiao, WANG Xin, ZHENG Yihui, et al. Short-term wind load forecasting based on improved LSSVM and error forecasting correction[J]. Power System Protection and Control, 2015, 43(11): 63-69.
[8] 马哲, 舒勤. 基于ESPRIT分解算法的短期电力负荷预测[J]. 电力系统保护与控制, 2015, 43(7): 90-96.
MA Zhe, SHU Qin. Short term load forecasting based on ESPRIT integrated algorithm[J]. Power System Protection and Control, 2015, 43(7): 90-96.
[9] 汤宝平, 董绍江, 马靖华. 基于独立分量分析的EMD模态混叠消除方法研究[J]. 仪器仪表学报, 2012, 33(7): 1477-1482.
TANG Baoping, DONG Shaojiang, MA Jinghua. Study on the method for eliminating mode mixing of empirical mode decomposition based on independent component analysis[J]. Chinese Journal of Scientific Instrument, 2012, 33(7): 1477-1482.
[10] 周潮, 邢文洋, 李宇龙. 电力系统负荷预测方法综述[J]. 电源学报, 2012, 6: 32-39.
ZHOU Chao, XING Wenyang, LI Yulong. Summarization on load forecasting method of electrical power system[J]. Journal of Power Supply, 2012, 6: 32-39.
[11] HAGAN M T, BEHR S M. The time series approach to short term load forecasting[J]. IEEE Transactions on Power Systems, 1987, 2(3): 785-791.
[12] NOZAKI K, ISHIBUCHI H, TANAKA H. A simple but powerful heuristic method for generating fuzzy rules from numerical data[J]. Fuzzy Sets and Systems, 1997, 86(3): 251-270.
[13] SADAEI H J, ENAYATIFAR R, ABDULLAH A H, et al. Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search[J]. International Journal of Electrical Power & Energy Systems, 2014, 62: 118-129.
[14] CHEN T, WANG Y C. Long-term load forecasting by a collaborative fuzzy-neural approach[J]. International Journal of Electrical Power & Energy Systems, 2012, 43(1): 454-464.
[15] EBADZADEH M M, SALIMI-BADR A. CFNN: Correlated fuzzy neural network[J]. Neurocomputing, 2015, 148: 430-444.
[16] MARQUARDT D W. An algorithm for least-squares estimation of nonlinear parameters[J]. Journal of the Society for Industrial & Applied Mathematics, 1963, 11(2): 431-441.
[17] 韩义波, 韩璞. 基于一种新的正交优化的群智能优化算法[J]. 计算机应用研究, 2015, 32(1): 71-74.
HAN Yibo, HAN Pu. Orthogonal optimization based swarm intelligent optimization algorithm[J]. Application R esearch of Computers, 2015, 32(1): 71-74.
[18] 秦全德, 程适, 李丽, 等. 人工蜂群算法研究综述[J]. 智能系统学报, 2014, 9(2): 127-135.
QIN Quande, CHENG Shi, LI Li, et al. Artificial bee colony algorithm: a survey[J]. CAAI Transactions on Intelligent Systems, 2014, 9(2): 127-135.
[19] 陈杰, 沈艳霞, 陆欣. 基于信息反馈和改进适应度评价的人工蜂群算法[EB/OL]. (2016-03-15)[2016-06-01] http://www.cnki.net/kcms/detail/23.1538.TP.20160315.1051.002.html
[20] GAO Weifeng, LIU Sanyang. A modified artificial bee colony algorithm[J]. Computers and Operations Research, 2011, 39(3), 687-697.
[21] FIGUEROA-GARCíA J C, OCHOA-REY C M, AVELLANEDA-GONZáLEZ J. Rule generation of fuzzy logic systems using a self-organized fuzzy neural network[J]. Neurocomputing, 2015, 151(3): 955-962.
[22] 陈茹雯. 非线性自回归时序模型分析[M]. 南京: 东南大学出版社, 2011.