Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines

Accurate prediction of substation project cost is helpful to improve the investment management and sustainability. It is also directly related to the economy of substation project. Ensemble Empirical Mode Decomposition (EEMD) can decompose variables with non-stationary sequence signals into significant regularity and periodicity, which is helpful in improving the accuracy of prediction model. Adding the Gauss perturbation to the traditional Cuckoo Search (CS) algorithm can improve the searching vigor and precision of CS algorithm. Thus, the parameters and kernel functions of Support Vector Machines (SVM) model are optimized. By comparing the prediction results with other models, this model has higher prediction accuracy.

[1]  Saida Makhloufi,et al.  Three powerful nature-inspired algorithms to optimize power flow in Algeria's Adrar power system , 2016 .

[2]  Jouni Helske,et al.  Ensemble Empirical Mode Decomposition (EEMD) and Its CompleteVariant (CEEMDAN) , 2015 .

[3]  Wan Zurina Wan Jaafar,et al.  Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test , 2016 .

[4]  Wei Meng,et al.  Predicting Bio-indicators of Aquatic Ecosystems Using the Support Vector Machine Model in the Taizi River, China , 2017 .

[5]  Huiru Zhao,et al.  Risk Evaluation of a UHV Power Transmission Construction Project Based on a Cloud Model and FCE Method for Sustainability , 2015 .

[6]  Jie Wu,et al.  Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms , 2016 .

[7]  Nasrudin Abd Rahim,et al.  Long-term electric energy consumption forecasting via artificial cooperative search algorithm , 2016 .

[8]  J. Abdul Jaleel,et al.  Reliability Evaluation of 220 kV Substation Using Fault Tree Method and Its Prediction Using Neural Networks , 2013 .

[9]  Zhengyan Shao,et al.  A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction , 2015 .

[10]  Yongquan Zhou,et al.  A Novel Cuckoo Search Optimization Algorithm Base on Gauss Distribution , 2012 .

[11]  Baabak Ashuri,et al.  Highway Construction Cost Forecasting Using Vector Error Correction Models , 2016 .

[12]  Wang Mianbi.  Index Evaluation System of Power Transmission Project Cost Based on Support Vector Machine Method , 2014 .

[13]  Jin-peng Liu,et al.  The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection , 2017 .

[14]  Jing Ma,et al.  Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting , 2017 .

[15]  Di Wu,et al.  EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM , 2017 .

[16]  V. Fassouli,et al.  Evaluation and Selection of Indicators for Land Degradation and Desertification Monitoring: Methodological Approach , 2014, Environmental Management.

[17]  A. García-Berrocal,et al.  Cofrentes nuclear power plant instability analysis using ensemble empirical mode decomposition (EEMD) , 2017 .

[18]  Zhongfu Tan,et al.  A Systematical Framework of Schedule Risk Management for Power Grid Engineering Projects’ Sustainable Development , 2014 .

[19]  U. Rajendra Acharya,et al.  Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals , 2016, Neural Computing and Applications.

[20]  Jiangze Du,et al.  Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology , 2016 .

[21]  Alireza Bahadori,et al.  Prediction of the properties of brines using least squares support vector machine (LS-SVM) computational strategy , 2015 .

[22]  Lili Su,et al.  Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting , 2017 .

[23]  Ke Xu,et al.  Forecasting Tendency of the Transmission Line Project Cost Index Using Markov Chain , 2014 .

[24]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[25]  Yi Liang,et al.  Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search , 2016 .

[26]  Nima Amjady,et al.  Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine , 2017 .

[27]  Antonio Ficarella,et al.  Cavitation Regime Detection by LS-SVM and ANN With Wavelet Decomposition Based on Pressure Sensor Signals , 2015, IEEE Sensors Journal.

[28]  Qian Fang Pei,et al.  Study on the Management of Construction Cost in Substation Project , 2014 .

[29]  Zhiqiang Deng,et al.  How Reliable Are ANN, ANFIS, and SVM Techniques for Predicting Longitudinal Dispersion Coefficient in Natural Rivers? , 2016 .

[30]  Edward J. Williams,et al.  Cuckoo optimization algorithm with penalty function for combined heat and power economic dispatch problem , 2015 .

[31]  A. Renzaho,et al.  Prevalence of diabetes in Zimbabwe: a systematic review with meta-analysis , 2014, International Journal of Public Health.

[32]  Wei Sun,et al.  Daily PM2.5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm. , 2017, Journal of environmental management.

[33]  Sungwoo Moon,et al.  Stochastic Forecast of Construction Cost Index Using a Cointegrated Vector Autoregression Model , 2013 .

[34]  Hasmat Malik,et al.  EMD and ANN based intelligent fault diagnosis model for transmission line , 2017, J. Intell. Fuzzy Syst..

[35]  Bong-Hwan Koh,et al.  Fault Detection of Bearing Systems through EEMD and Optimization Algorithm , 2017, Sensors.