An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting

Abstract Accurate and stable annual electricity consumption forecasting play vital role in modern social and economic development through providing effective planning and guaranteeing a reliable supply of sustainable electricity. However, establishing a robust method to improve prediction accuracy and stability simultaneously of electricity consumption forecasting has been proven to be a highly challenging task. Most previous researches only pay more attention to enhance prediction accuracy, which usually ignore the significant of forecasting stability, despite its importance to the effectiveness of forecasting models. Considering the characteristics of annual power consumption data as well as one criterion i.e. accuracy or stability is insufficient, in this study a novel hybrid forecasting model based on an improved grey forecasting mode optimized by multi-objective ant lion optimization algorithm is successfully developed, which can not only be utilized to dynamic choose the best input training sets, but also obtain satisfactory forecasting results with high accuracy and strong ability. Case studies of annual power consumption datasets from several regions in China are utilized as illustrative examples to estimate the effectiveness and efficiency of the proposed hybrid forecasting model. Finally, experimental results indicated that the proposed forecasting model is superior to the comparison models.

[1]  Lifeng Wu,et al.  The effect of sample size on the grey system model , 2013 .

[2]  Jing Zhao,et al.  An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed , 2016 .

[3]  Sifeng Liu,et al.  Discrete grey forecasting model and its optimization , 2009 .

[4]  Diyar Akay,et al.  Grey prediction with rolling mechanism for electricity demand forecasting of Turkey , 2007 .

[5]  Chao Liu,et al.  Short-term prediction of wind power using EMD and chaotic theory , 2012 .

[6]  Chen Hua-you.  Research on Superior Combination Forecasting Model Based on Forecasting Effective Measure , 2002 .

[7]  Wangshu Sun,et al.  Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China , 2016 .

[8]  Chen Li,et al.  Research and application of an innovative combined model based on a modified optimization algorithm for wind speed forecasting , 2018, Measurement.

[9]  Hongmin Li,et al.  Research and application of a combined model based on variable weight for short term wind speed forecasting , 2018 .

[10]  Shu Wang,et al.  Predicting Beijing's tertiary industry with an improved grey model , 2017, Appl. Soft Comput..

[11]  Jianzhou Wang,et al.  A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting , 2015 .

[12]  A. Lewis,et al.  Multi-Objective Optimization in High Frequency Electromagnetics—An Effective Technique for Smart Mobile Terminal Antenna (SMTA) Design , 2009, IEEE Transactions on Magnetics.

[13]  Yagang Zhang,et al.  Lorenz Wind Disturbance Model Based on Grey Generated Components , 2014 .

[14]  Chengshi Tian,et al.  A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting , 2018 .

[15]  Rui Wang,et al.  Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting , 2017, Entropy.

[16]  Zheng-xin Wang,et al.  Grey forecasting method of quarterly hydropower production in China based on a data grouping approach , 2017 .

[17]  Jianzhou Wang,et al.  Multi-step ahead forecasting in electrical power system using a hybrid forecasting system , 2018, Renewable Energy.

[18]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[19]  Jianzhou Wang,et al.  Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system , 2018 .

[20]  Pradeep Jangir,et al.  Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems , 2016, Applied Intelligence.

[21]  Sifeng Liu,et al.  A novel interval grey prediction model considering uncertain information , 2013, J. Frankl. Inst..

[22]  Hongbo Zhou,et al.  An optimized nonlinear grey Bernoulli model and its applications , 2016, Neurocomputing.

[23]  Tianyou Chai,et al.  hybrid evolutionary multiobjective optimization strategy for the dynamic ower supply problem in magnesia grain manufacturing , 2013 .

[24]  Coşkun Hamzaçebi,et al.  Forecasting the annual electricity consumption of Turkey using an optimized grey model , 2014 .

[25]  Jianzhou Wang,et al.  Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China , 2012 .

[26]  Cheng-Chin Chiang,et al.  A hybrid approach of neural networks and grey modeling for adaptive electricity load forecasting , 2006, Neural Computing & Applications.

[27]  Chen Wang,et al.  A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting , 2016 .

[28]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[29]  Xiaobo Zhang,et al.  Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm , 2017 .

[30]  Wei-Chiang Hong,et al.  Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm , 2016 .

[31]  Jianzhou Wang,et al.  An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms , 2018 .

[32]  Xin Ma,et al.  Application of a novel time-delayed polynomial grey model to predict the natural gas consumption in China , 2017, J. Comput. Appl. Math..

[33]  Qin Liu,et al.  Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model , 2014 .

[34]  Chaoqing Yuan,et al.  Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model , 2015 .

[35]  Bo Zeng,et al.  Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator , 2018 .

[36]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[37]  Xin Ma,et al.  The kernel-based nonlinear multivariate grey model , 2018 .

[38]  Xiaobo Zhang,et al.  A novel decomposition‐ensemble model for forecasting short‐term load‐time series with multiple seasonal patterns , 2018, Appl. Soft Comput..

[39]  Jianzhou Wang,et al.  A novel hybrid system based on a new proposed algorithm-Multi-Objective Whale Optimization Algorithm for wind speed forecasting , 2017 .

[40]  Huiru Zhao,et al.  An optimized grey model for annual power load forecasting , 2016 .

[41]  Jianzhou Wang,et al.  A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm , 2018 .

[42]  Lee-Ing Tong,et al.  Forecasting energy consumption using a grey model improved by incorporating genetic programming , 2011 .

[43]  Yunzhen Xu,et al.  Air quality early-warning system for cities in China , 2017 .

[44]  Leandro dos Santos Coelho,et al.  Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization , 2016, Expert Syst. Appl..

[45]  Yu Jin,et al.  The early-warning system based on hybrid optimization algorithm and fuzzy synthetic evaluation model , 2018, Inf. Sci..

[46]  Feng Liu,et al.  A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting , 2017 .

[47]  Wei Cui,et al.  Non-homogenous discrete grey model with fractional-order accumulation , 2014, Neural Computing and Applications.

[48]  Paul J. Gemperline,et al.  Nonlinear multivariate calibration using principal components regression and artificial neural networks , 1991 .

[49]  Shouyang Wang,et al.  A Clustering-Based Nonlinear Ensemble Approach for Exchange Rates Forecasting , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[50]  Jianzhou Wang,et al.  Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting , 2017 .

[51]  Shaolong Sun,et al.  A new dynamic integrated approach for wind speed forecasting , 2017 .

[52]  Chen Wang,et al.  Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting , 2016 .

[53]  Keith W. Hipel,et al.  Forecasting China's electricity consumption using a new grey prediction model , 2018 .

[54]  Xin Ma,et al.  A novel kernel regularized nonhomogeneous grey model and its applications , 2017, Commun. Nonlinear Sci. Numer. Simul..

[55]  Chuan Li,et al.  Forecasting the natural gas demand in China using a self-adapting intelligent grey model , 2016 .

[56]  Chen-Fang Tsai,et al.  Dynamic grey platform for efficient forecasting management , 2015, J. Comput. Syst. Sci..

[57]  Zheng-Xin Wang,et al.  An optimized Nash nonlinear grey Bernoulli model for forecasting the main economic indices of high technology enterprises in China , 2013, Comput. Ind. Eng..