考慮氣象因素的PCA-BP神經(jīng)網(wǎng)絡(luò)短期負荷預(yù)測
王海峰，姜雲(yún)騰，李萍
（寧夏大學(xué) 物理與電子電氣工程學(xué)院，寧夏 銀川 750021）
摘 要：為有效提高電力系統(tǒng)短期負荷預(yù)測精度及效率，提出一種基于主成分分析的BP神經(jīng)網(wǎng)絡(luò)短期負荷預(yù)測優(yōu)化算法。利用主成分分析法將多個原始變量降維成少數(shù)彼此獨立的變量作為輸入，并根據(jù)各主成分的貢獻率來確定網(wǎng)絡(luò)的結(jié)構(gòu)，有效解決BP網(wǎng)絡(luò)預(yù)測精度與效率不高問題。在考慮氣象因素的影響下通過對某地區(qū)歷史負荷數(shù)據(jù)進行訓(xùn)練仿真，平均預(yù)測精度接近98%，預(yù)測程序運行效率提高兩倍以上，仿真結(jié)果表明，該模型在效率和預(yù)測精度方面優(yōu)于BP神經(jīng)網(wǎng)絡(luò)模型。
關(guān)鍵詞：主成分分析；負荷預(yù)測；BP 神經(jīng)網(wǎng)絡(luò)
中圖分類號：TM715 文獻標識碼：A 文章編號：1007-3175(2018)07-0038-04
Short-Term Load Forecasting Based on Principal Component Analysis-Back
Propagation Neural Network Considering Meteorological Factor
WANG Hai-feng, JANG Yun-teng, LI Ping
（School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China）
Abstract: In order to effectively improve the accuracy and efficiency of short-term load forecasting, this paper proposed a back propagation(BP) neural network short-term load forecasting optimization algorithm based on the principal component analysis. The principal component analysis method was used to reduce a number of original variables into a few independent variables as input, and to determine the network structure according to the contribution rate of the main components, and effectively solve the problem of low prediction accuracy and efficiency of BP network. Taking the influence of meteorological factors into consideration, the results of training and simulation of historical load data in a certain area show that the average prediction accuracy is close to 98%, which is more than two times of the running efficiency of the forecast program. The simulation results show that the model is superior to the BP neural network model in efficiency and prediction accuracy.
Key words: principal component analysis; load forecasting; back propagation neural network
參考文獻
[1] 廖旎煥，胡智宏，馬瑩瑩，等. 電力系統(tǒng)短期負荷預(yù)測方法綜述[J]. 電力系統(tǒng)保護與控制，2011，39(1) ：147-152.
[2] 呂躍春，邵常寧，劉欣宇，等. 粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)在短期負荷預(yù)測誤差修正模型中的應(yīng)用研究[J]. 電氣應(yīng)用，2015，34(5) ：28-32.
[3] 張奔，史沛然，蔣超. 氣象因素對京津唐電網(wǎng)夏季負荷特性影響分析[J]. 電力自動化設(shè)備，2013，33(12) ：140-144.
[4] 李培強，李慧，李欣然. 基于靈敏度與相關(guān)性的綜合負荷模型參數(shù)優(yōu)化辨識策略[J]. 電工技術(shù)學(xué)報，2016，31(16) ：181-188
[5] 李龍，魏靖，黎燦兵，等. 基于人工神經(jīng)網(wǎng)絡(luò)的負荷模型預(yù)測[J]. 電工技術(shù)學(xué)報，2015，30(8) ：225-230.
[6] 杜莉，張建軍. 神經(jīng)網(wǎng)絡(luò)在電力負荷預(yù)測中的應(yīng)用研究[J]. 計算機仿真，2015，30(8) ：225-230.
[7] 隋惠惠. 基于BP神經(jīng)網(wǎng)絡(luò)的短期電力負荷預(yù)測的研究[D]. 哈爾濱：哈爾濱工業(yè)大學(xué)，2015.
[8] 許童羽，馬藝銘，曹英麗，等. 基于主成分分析和遺傳優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的光伏輸出功率短期預(yù)測[J]. 電力系統(tǒng)保護與控制，2016，44(22) ：90-95.
