A novel power-driven fractional accumulated grey model and its application in forecasting wind energy consumption of China

Wind energy is one of the most important renewable resources and plays a vital role in reducing carbon emission and solving global warming problem. Every country has made a corresponding energy policy to stimulate wind energy industry development based on wind energy production, consumption, and distribution. In this paper, we focus on forecasting wind energy consumption from a macro perspective. A novel power-driven fractional accumulated grey model (PFAGM) is proposed to solve the wind energy consumption prediction problem with historic annual consumption of the past ten years. PFAGM model optimizes the grey input of the classic fractional grey model with an exponential term of time. For boosting prediction performance, a heuristic intelligent algorithm WOA is used to search the optimal order of PFAGM model. Its linear parameters are estimated by using the least-square method. Then validation experiments on real-life data sets have been conducted to verify the superior prediction accuracy of PFAGM model compared with other three well-known grey models. Finally, the PFAGM model is applied to predict China’s wind energy consumption in the next three years.

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

[2]  Paulo Sérgio Lucio,et al.  A hybrid model based on time series models and neural network for forecasting wind speed in the Brazilian northeast region , 2018, Sustainable Energy Technologies and Assessments.

[3]  Yong Wang,et al.  MODELING THE NONLINEAR FLOW FOR A MULTIPLE-FRACTURED HORIZONTAL WELL WITH MULTIPLE FINITE-CONDUCTIVITY FRACTURES IN TRIPLE MEDIA CARBONATE RESERVOIR , 2018 .

[4]  Qiang Ji,et al.  Forecasting China's natural gas demand based on optimised nonlinear grey models , 2017 .

[5]  Xinping Xiao,et al.  A novel fractional grey system model and its application , 2016 .

[6]  Zheng-xin Wang,et al.  A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors , 2018, Energy.

[7]  Zhibin Liu,et al.  Predicting the Cumulative Oil Field Production Using the Novel Grey ENGM Model , 2016 .

[8]  Yi-Chung Hu,et al.  Electricity consumption prediction using a neural-network-based grey forecasting approach , 2017, J. Oper. Res. Soc..

[9]  Xiaobing Kong,et al.  Wind speed prediction using reduced support vector machines with feature selection , 2015, Neurocomputing.

[10]  Jianzhou Wang,et al.  A novel hybrid model for short-term wind power forecasting , 2019, Appl. Soft Comput..

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

[12]  Abheejeet Mohapatra,et al.  Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting , 2019, Renewable Energy.

[13]  Xin Ma,et al.  A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China , 2019, Energy.

[14]  Lianhui Li,et al.  A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting , 2018, PloS one.

[15]  Yingjie Yang,et al.  Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China , 2018 .

[16]  Jie Xia,et al.  Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity , 2019, Journal of Cleaner Production.

[17]  Jianzhou Wang,et al.  Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms , 2018 .

[18]  Lei Tang,et al.  Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory , 2019, Energy.

[19]  Baozhen Yao,et al.  Application of Discrete Mathematics in Urban Transportation System Analysis , 2014 .

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

[21]  Wenqing Wu,et al.  The conformable fractional grey system model. , 2018, ISA transactions.

[22]  Q. Wang,et al.  Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM , 2019, Energy.

[23]  Ning Xu,et al.  Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China , 2017 .

[24]  Hui Liu,et al.  Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network , 2018 .

[25]  Bo Zeng,et al.  Improved multi-variable grey forecasting model with a dynamic background-value coefficient and its application , 2018, Comput. Ind. Eng..

[26]  Yong Wang,et al.  The novel fractional discrete multivariate grey system model and its applications , 2019, Applied Mathematical Modelling.

[27]  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..

[28]  Feng Qian,et al.  Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm , 2017 .

[29]  Hongying Zhao,et al.  Discrete grey model with the weighted accumulation , 2019, Soft Computing.

[30]  Sifeng Liu,et al.  Grey system model with the fractional order accumulation , 2013, Commun. Nonlinear Sci. Numer. Simul..

[31]  K. H. Solangi,et al.  A review on global wind energy policy , 2010 .

[32]  Naiming Xie,et al.  A nonlinear grey forecasting model with double shape parameters and its application , 2019, Appl. Math. Comput..

[33]  Ling Xiao,et al.  An improved combination approach based on Adaboost algorithm for wind speed time series forecasting , 2018 .

[34]  Wenqing Wu,et al.  Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption , 2018, Energy.

[35]  Bo Zeng,et al.  A new multivariable grey prediction model with structure compatibility , 2019, Applied Mathematical Modelling.

[36]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[37]  Jennie Malboeuf.  Algorithm , 1994, Neurology.

[38]  Peng-Yu Chen,et al.  Foundation Settlement Prediction Based on a Novel NGM Model , 2014 .

[39]  Reinaldo Castro Souza,et al.  A bottom-up bayesian extension for long term electricity consumption forecasting , 2019, Energy.

[40]  Sifeng Liu,et al.  A self‐adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application , 2017 .

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

[42]  D. M. Vinod Kumar,et al.  Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction , 2018, Energy Conversion and Management.

[43]  杨俊,et al.  Forecasting natural gas consumption in China by Bayesian Model Averaging , 2015 .

[44]  Wei Zhang,et al.  Forecasting natural gas consumption in China by Bayesian Model Averaging , 2015 .

[45]  Ling Tang,et al.  A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting , 2014 .

[46]  Dingyu Xue,et al.  Continuous fractional-order grey model and electricity prediction research based on the observation error feedback , 2016 .

[47]  Jian Wang,et al.  A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm , 2018, Energy.

[48]  C. L. Philip Chen,et al.  Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting , 2015, IEEE Transactions on Sustainable Energy.

[49]  Pan Zhang.  Do energy intensity targets matter for wind energy development? Identifying their heterogeneous effects in Chinese provinces with different wind resources , 2019, Renewable Energy.

[50]  Hui Liu,et al.  Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods , 2019, Energy Conversion and Management.

[51]  V. Bianco,et al.  Electricity consumption forecasting in Italy using linear regression models , 2009 .

[52]  M. E. Günay,et al.  Forecasting annual natural gas consumption using socio-economic indicators for making future policies , 2019, Energy.

[53]  Seongkyu Yoon,et al.  A process optimization strategy of a pulsed-spray fluidized bed granulation process based on predictive three-stage population balance model , 2018 .

[54]  Jianchun Peng,et al.  A review of deep learning for renewable energy forecasting , 2019, Energy Conversion and Management.

[55]  Farhad Soleimanian Gharehchopogh,et al.  A comprehensive survey: Whale Optimization Algorithm and its applications , 2019, Swarm Evol. Comput..

[56]  Osamah Basheer Shukur,et al.  Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA , 2015 .

[57]  Rahmat-Allah Hooshmand,et al.  Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm , 2014 .

[58]  Haojun Tang,et al.  A novel framework for wind speed prediction based on recurrent neural networks and support vector machine , 2018, Energy Conversion and Management.

[59]  Zheng-Xin Wang,et al.  An improved grey multivariable model for predicting industrial energy consumption in China , 2016 .

[60]  Jianzhou Wang,et al.  Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China , 2019, Journal of Cleaner Production.

[61]  Dongxiao Niu,et al.  Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm , 2014 .