A genetic-algorithm-based remnant grey prediction model for energy demand forecasting

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants.

[1]  Jian Wang,et al.  Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks , 2010 .

[2]  Chao-Hung Wang,et al.  Using genetic algorithms grey theory to forecast high technology industrial output , 2008, Appl. Math. Comput..

[3]  M. Mao,et al.  Application of grey model GM(1, 1) to vehicle fatality risk estimation , 2006 .

[4]  Yi-Chung Hu,et al.  Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting , 2017 .

[5]  Shaowen Wang,et al.  A scalable parallel genetic algorithm for the Generalized Assignment Problem , 2015, Parallel Comput..

[6]  Yi-Chun Liao,et al.  Forecasting LCD TV Demand Using the Fuzzy Grey Model Gm(1, 1) , 2007, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

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

[8]  Yi-Chung Hu,et al.  Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models , 2017 .

[9]  M. Duran Toksarı,et al.  Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey , 2009 .

[10]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

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

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Yi-Chung Hu,et al.  Functional-link net with fuzzy integral for bankruptcy prediction , 2007, Neurocomputing.

[14]  Andrzej Osyczka,et al.  Evolutionary Algorithms for Single and Multicriteria Design Optimization , 2001 .

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

[16]  Peng Jin,et al.  A mega-trend-diffusion grey forecasting model for short-term manufacturing demand , 2016, J. Oper. Res. Soc..

[17]  J. Liu,et al.  A Grey Prediction Approach to Forecasting Energy Demand in China , 2010 .

[18]  L. Suganthi,et al.  Energy models for demand forecasting—A review , 2012 .

[19]  Jiuchang Wei,et al.  Work safety evaluation in Mainland China using grey theory , 2015 .

[20]  Nursel Öztürk,et al.  Grey modelling based forecasting system for return flow of end-of-life vehicles , 2017 .

[21]  Yi-Chung Hu,et al.  Flow-based grey single-layer perceptron with fuzzy integral , 2012, Neurocomputing.

[22]  Chun-An Chou,et al.  A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey , 2015 .

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

[24]  Yi Lin,et al.  Grey Information - Theory and Practical Applications , 2005, Advanced Information and Knowledge Processing.

[25]  Yi-Chung Hu,et al.  Recommendation using neighborhood methods with preference-relation-based similarity , 2014, Inf. Sci..

[26]  Jung-Hsien Chiang,et al.  Choquet fuzzy integral-based hierarchical networks for decision analysis , 1999, IEEE Trans. Fuzzy Syst..

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

[28]  V. Ediger,et al.  ARIMA forecasting of primary energy demand by fuel in Turkey , 2007 .

[29]  David E. Goldberg,et al.  Efficient Parallel Genetic Algorithms: Theory and Practice , 2000 .

[30]  Jesús M. Zamarreño,et al.  Prediction of hourly energy consumption in buildings based on a feedback artificial neural network , 2005 .

[31]  Ioannis G. Tsoulos,et al.  PDoublePop: An implementation of parallel genetic algorithm for function optimization , 2016, Comput. Phys. Commun..

[32]  Hisao Ishibuchi,et al.  Classification and modeling with linguistic information granules - advanced approaches to linguistic data mining , 2004, Advanced information processing.

[33]  Chaug,et al.  Improved grey prediction models for the trans ‐ pacific air passenger market , 2007 .

[34]  Li-Chang Hsu,et al.  Applying the Grey prediction model to the global integrated circuit industry , 2003 .

[35]  Peng Jiang,et al.  Forecasting energy demand using neural-network-based grey residual modification models , 2017, J. Oper. Res. Soc..

[36]  Chaug-Ing Hsu,et al.  IMPROVED GREY PREDICTION MODELS FOR THE TRANS-PACIFIC AIR PASSENGER MARKET , 1998 .

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

[38]  Bu-Sung Lee,et al.  Efficient Hierarchical Parallel Genetic Algorithms using Grid computing , 2007, Future Gener. Comput. Syst..

[39]  Wei-jie Yu,et al.  A parallel double-level multiobjective evolutionary algorithm for robust optimization , 2017, Appl. Soft Comput..

[40]  S. J. Feng,et al.  Forecasting the Energy Consumption of China by the Grey Prediction Model , 2012 .

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

[42]  Chia-Yon Chen,et al.  Applications of improved grey prediction model for power demand forecasting , 2003 .

[43]  M. Berthold,et al.  International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , 1998 .

[44]  Karlson Hargroves,et al.  Energy transformed: Sustainable energy solutions for climate change mitigation , 2007 .

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

[46]  Xue Yanfeng,et al.  Research on China's energy supply and demand using an improved Grey-Markov chain model based on wavelet transform , 2017 .

[47]  Y. Takefuji,et al.  Functional-link net computing: theory, system architecture, and functionalities , 1992, Computer.

[48]  Philippe Lauret,et al.  Bayesian neural network approach to short time load forecasting , 2008 .