A novel fractional discrete grey model with an adaptive structure and its application in electricity consumption prediction

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

[1]  Tzu-Li Tien,et al.  A new grey prediction model FGM(1, 1) , 2009, Math. Comput. Model..

[2]  Xianguo Wu,et al.  Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China , 2020 .

[3]  Wenqing Wu,et al.  Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model , 2019, Renewable Energy.

[4]  Naiming Xie,et al.  On the properties of small sample of GM(1,1) model , 2009 .

[5]  Y. Long,et al.  A factor-based bottom-up approach for the long-term electricity consumption estimation in the Japanese residential sector. , 2020, Journal of environmental management.

[6]  Wei-Chiang Hong,et al.  Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model , 2018, Applied Energy.

[7]  Zichen Zhang,et al.  A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting , 2018 .

[8]  Sifeng Liu,et al.  An approach to increase prediction precision of GM(1, 1) model based on optimization of the initial condition , 2010, Expert Syst. Appl..

[9]  Aven Satre-Meloy,et al.  Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models , 2019, Energy.

[10]  Sifeng Liu,et al.  A Gray Model With a Time Varying Weighted Generating Operator , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[11]  Xinping Xiao,et al.  Error and its upper bound estimation between the solutions of GM(1, 1) grey forecasting models , 2014, Appl. Math. Comput..

[12]  Czesław Cempel,et al.  Using a set of GM(1,1) models to predict values of diagnostic symptoms , 2015 .

[13]  Der-Chiang Li,et al.  Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case , 2012 .

[14]  Chun-Wu Yeh,et al.  An improved grey-based approach for early manufacturing data forecasting , 2009, Comput. Ind. Eng..

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

[16]  Wei Meng,et al.  A self-adaptive intelligence grey predictive model with alterable structure and its application , 2016, Eng. Appl. Artif. Intell..

[17]  Jin Xu,et al.  Improvement of grey models by least squares , 2011, Expert Syst. Appl..

[18]  Liu Si-feng,et al.  The GM models that x(n) be taken as initial value , 2004 .

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

[20]  Hussain Ali Bekhet,et al.  CO2 emissions, energy consumption, economic growth, and financial development in GCC countries: Dynamic simultaneous equation models , 2017 .

[21]  Naiming Xie,et al.  Optimal solution for novel grey polynomial prediction model , 2018, Applied Mathematical Modelling.

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

[23]  Jing Zhao,et al.  Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China , 2012 .

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

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

[26]  Lei Wen,et al.  Forecasting the annual household electricity consumption of Chinese residents using the DPSO-BP prediction model , 2020, Environmental Science and Pollution Research.

[27]  Shoujun Li,et al.  A novel varistructure grey forecasting model with speed adaptation and its application , 2020, Math. Comput. Simul..

[28]  Tao Yu,et al.  Model GM(1, 1, ß) and its applicable region , 2013, Grey Syst. Theory Appl..

[29]  Minda Ma,et al.  What drives the carbon mitigation in Chinese commercial building sector? Evidence from decomposing an extended Kaya identity. , 2018, The Science of the total environment.

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

[31]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

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

[33]  Vivek Srikumar,et al.  Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks , 2018 .

[34]  Chong Liu,et al.  Forecasting annual electricity consumption in China by employing a conformable fractional grey model in opposite direction , 2020 .

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

[36]  Jie Cui,et al.  A novel grey forecasting model and its optimization , 2013 .

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

[38]  Wenyuan Wang,et al.  Machine learning method for energy consumption prediction of ships in port considering green ports , 2020 .

[39]  Chaoqing Yuan,et al.  On novel grey forecasting model based on non-homogeneous index sequence , 2013 .