Substation Engineering Cost Forecasting Method Based on Modified Firefly Algorithm and Support Vector Machine

doi:10.11930/j.issn.1004-9649.2017.03.168.06

Abstract

Abstract: The cost level of substation engineering is closely related to the integrated economy of power grid projects, and the cost level forecasting is a crucial tool for controlling cost and improving cost rationality. Based on the conventional firefly algorithm, the Gaussian Disturbance is introduced into the firefly algorithm to improve the update equation, which aims to improve the searching ability and optimize the SVM parameters. By operating the Schaffer testing function, it is discovered that the Gaussian disturbance firefly algorithm has better convergence rate and searching ability. The case study of substation engineering in Guangdong Province further proves that the proposed model has higher forecasting accuracy and effectiveness

Key words: firefly algorithm, support vector machine, Gaussian disturbance, substation engineering, cost forecasting

CLC Number:

TM714
TM744

SONG Zongyun, NIU Dongxiao, XIAO Xinli, ZHU Lin. Substation Engineering Cost Forecasting Method Based on Modified Firefly Algorithm and Support Vector Machine[J]. Electric Power, 2017, 50(3): 168-173.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2017.03.168.06

https://www.electricpower.com.cn/EN/Y2017/V50/I3/168

References

[1] VAPNIK V N. The nature of statistical learning theory [M]. Netherlands: Springer Science & Business Media, 2000.
[2] 易智婷,秦博,张平朗. 输电线路大跨越工程造价特点及影响因素分析[J]. 南方电网技术,2008,2（5）：76-78.
YI Zhiting, QIN Bo, ZHANG Pinglang. Analysis on construction cost characteristics and influence factors for the long span project of transmission lines[J]. Southern Power System Technology, 2008, 2(5): 76-78.
[3] 孟凡喜,屈鸿,侯孟书. 基于 GA 和 SVM 的电力负荷预测方法研究[J]. 计算机科学,2014,41（S1）：91-93,117.
MENG Fanxi, QU Hong, HOU Mengshu. Method of short-term load forecasting based on GA and SVM [J]. Computer Science, 2014, 41(S1): 91-93, 117.
[4] 潘磊,李丽娟,丁婷婷,等. 基于改进PSO算法和LS-SVM的短期电力负荷预测[J]. 工矿自动化,2012,38（9）：55-59.
PAN Lei, LI Lijuan, DING Tingting, et al . Forecasting of short- term power load based on improved PSO algorithm and LS-SVM [J]. Industry and Mine Automation, 2012, 38(9): 55-59.
[5] 陈超,黄国勇,邵宗凯,等. 基于日特征量相似日的 PSO-SVM 短期负荷预测[J]. 中国电力,2013,46（7）：91-94.
CHEN Chao, HUANG Guoyong, SHAO Zongkai, et al . Short-term load forecasting for similar days based on PSO-SVM and daily feature vector [J]. Electric Power, 2013, 46(7): 91-94.
[6] 李娟. 基于最大最小萤火虫算法优化BP神经网络的物流需求预测算法研究[J]. 经营管理者,2015（23）：4-5.
LI Juan. Logistics demand forecasting based on maximum- minimum firefly algorithm and BP[J]. Manager’s Journal, 2015, (23): 4-5.
[7] 杨单,李超锋,杨健. 基于改进混沌萤火虫算法的云计算资源调度[J]. 计算机工程,2015,41（2）：17-20,25.
YANG Dan, LI Chaofeng, YANG Jian. Cloud computing resource scheduling based on improved chaotic firefly algorithm [J]. Computer engineering, 2015, 41(2): 17-20, 25.
[8] 李明富,张玉彦,马建华,等. 基于变参数萤火虫算法和Maklink图的路径规划研究[J]. 机械科学与技术,2015,34（11）：1728-1732.
LI Mingfu, ZHANG Yuyan, MA Jianhua, et al . Research on path planning based on variable parameters firefly algorithm and Maklink gragh[J]. Mechanical Science and Technology for Aerospace Engineering, 2015, 34(11): 1728-1732.
[9] 朱德刚,孙辉,赵嘉,等. 基于高斯扰动的粒子群优化算法[J]. 计算机应用,2014,34（3）：754-759.
ZHU Degang, SUN Hui, ZHAO Jia, et al . Particle swarm optimization algorithm based on Gaussian disturbance [J]. Journal of Computer Application, 2014, 34(3): 754-759.
[10] YANG X S. Firefly algorithms for multimodal optimization [M]. Sapporo: Springer, 2009: 169-178.
[11] ROBERTS I, KAHN J M, BOERTJES D. Convex channel power optimization in nonlinear WDM systems using gaussian noise model [J]. Journal of Lightwave Technology, 2016, 34(13): 1-1.
[12] 贺兴时,丁文静,杨新社. 基于模拟退火高斯扰动的蝙蝠优化算法[J]. 计算机应用研究,2014,31（2）：392-397.
HE Xingshi, DING Wenjing, YANG Xinshe. Bat algorithm based on simulated annealing and Gaussian perturbations[J]. Application Research of Computers, 2014, 31(2): 392-397.
[13] 王芸,孙辉. 一种具有速度扰动的高斯学习粒子群优化算法[J]. 小型微型计算机系统,2015,36（7）：1521-1525.
WANG Yun, SUN Hui. Gaussian learning particle swarm optimization algorithm based on speed disturbance [J]. Journal of Chinese Computer Systems, 2015, 36(7): 1521-1525.
[14] 陈云翔,李琳,李千,等. 基于改进差分进化算法的飞行控制律评估方法[J]. 航空学报,2013,34（6）：1261-1268.
CHEN Yunxiang, LI Lin, LI Qian, et al . Evaluation method for flight control law based on modified differential evolution algorithm [J]. Acta Aeronautica ET Astronautica Sinica, 2013, 34(6): 1261-1268.
[15] LIU X, LI N, HU Y. Combining inferences on the common mean of several inverse Gaussian distributions based on confidence distribution [J]. Statistics & Probability Letters. 2015, 105: 136-142.
[16] CHEN X, ZHAO J, HU W, et al . Short-term wind speed forecasting using decomposition-based neural networks combining abnormal detection method[J]. Abstract and Applied Analysis, 2014(1): 1-21.
[17] 黄元生,邓佳佳,苑珍珍. 基于 ARMA 误差修正和自适应粒子群优化的 SVM 短期负荷预测[J]. 电力系统保护与控制,2011,39（14）：26-32.
HUANG Yuansheng, DENG Jiajia, YUAN Zhenzhen. SVM short- term load forecasting based on ARMA error calibration and the adaptive particle swarm optimization [J]. Power System Protection and Control, 2011, 39(14): 26-32.
[18] 陈剑勇,苏浩益. 结合支持向量机和马尔可夫链算法的中长期电力负荷预测模型[J]. 南方电网技术,2012,6（1）：54-58.
CHEN Jianyong, SU Haoyi. A forecasting model of medium/long term power load in combination of the support vector machine and markov Chain algorithms[J]. Southern Power System Technology, 2012, 6(1): 54-58.