Linking the Researchers, Developing the Innovations Manuscripts submittal opens till 15th August, 2017. Please submit your papers at or

  • Volume 2014

    G M (1, 1) Analysis and Forecasting for Efficient Energy Production and Consumption
    (International Journal of Business, Economics and Management Works)

    Vol. 1, Issue 1, PP. 6-11, Nov. 2014
    Keywords: Grey Models, Auto Regressive Moving Average, Model accuracy, Electricity Demands

    Download PDF


    Electricity Generation and Forecasting is prerequisite to enhance industrialization, farming and residential requirement of one’s nation. It has great impact on both nation’s economy and standard of living that can be achieved through new forecasting techniques, enhanced electricity generation methodologies and better electricity conservation techniques. Most of the countries are allocating significant amount for power generation and forecasting from nation’s annual budgets. Much research work has been carried out to analyze and propose innovative methodologies for efficient electricity generation and forecasting. In our approach, Grey Model (1, 1) based on grey system has been used for forecasting results. Performance of the proposed technique has been compared with existing Auto regressive moving average forecasting model. Annual power generation and forecasting data in Sri Lanka were used as our case study. Unexpected power demands with non-systematic behaviour patterns motivate to use GM (1, 1) models. MAPE (Mean absolute percentage error), MSD (Mean absolute deviation) and MSE (Mean squared error) accuracy testing result shows that GM (1, 1) is outperformed compared with model fitting and model forecasting.  


    1. R.M. Kapila Taranga Ratnayaka: School of Economics, Wuhan University of Technology, Wuhan, PR China,
    2. D.M Kumudu Nadeeshani Seneviratna: School of Economics, Wuhan University of Technology, Wuhan, PR China,  

    Full Text


    R.M. Kapila Tharanga Rathnayaka, D.M Kumudu Nadeeshani Seneviratna "G M (1, 1) Analysis and Forecasting for Efficient Energy Production and Consumption" International Journal of Business, Economics and Managment works, Vol. 1, Issue 1, PP. 6-11, Nov. 2014


    1. [1]        Ping Zhanga, Hui Wang, "Fuzzy Wavelet Neural Networks for City Electric Energy Consumption Forecasting", Energy Procedia,Volume 17, Part B, 2012, Pages 1332–1338
    2. [2]        Zaid Mohamed and Pat Bodger, "Forecasting Electricity Consumption: A Comparison of Models for New Zealand",ir. bitstream/10092/821/1/12593635_C47.pdf Michael Silver, 2011
    3. [3]        S. Jebaraja and S. Iniyan, "A review of energy models", Renewable and Sustainable Energy Reviews, Volume 10, Issue 4, August 2006, Pages 281–311
    4. [4]        Huang M, He Y, Cen H. Predictive analysis on electric power supply and demand in China. Renew Energy 2009;32:1165–7.
    5. [5]        Tianxiang Yao; Zaiwu Gong  and Hong Gao, "GenerGrey Systems and Intelligent Services (GSIS), 2011 IEEE International Conference , Page(s): 204 - 208 .
    6. [6]        R.M Kapila Tharanga  Rathnayaka, “Cross-Cultural Dimensions of Business Communication: Evidence from Sri Lanka”, International Review of Management and Business Research,  3(3), 1579-1587, 2014; ISSN: 2306-9007, 2014, .
    7. [7]        Duan Li-zhong, Duan Gu-na,Zhai Guang-qian, Zhang Ying, Xuan Chun-yu,Geng Hao, "The Grey Relational Analysis of influential factors for Chinese medicine in General Hospital ",Grey Systems and Intelligent Services (GSIS), 2011 IEEE International Conference onDigital Object Identifier: 10.1109/GSIS.2011.6044143: 2011 , Page(s): 23 - 29 .
    8. [8]        Li Junliang, Liao Ruiquan and Chen Xiaochun, "Generalized accumulated GM(1,1) cosine model and its application ",Grey Systems and Intelligent Services (GSIS), 2011 IEEE International Conference on Digital Object Identifier: 10.1109/GSIS.2011.6044066, Publication Year: 2011 , Page(s): 242 - 245.
    9. [9]        Ujjwal Kumar and V.K. Jain, "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India",2009, Energy 35 (2010) 1709–1716 .
    10. [10]     R.M Kapila Tharanga  Rathnayaka, D.M. Kumudu Nadeeshani Seneviratne and Zhong- jun Wang, “An Investigation of Statistical Behaviors of the Stock Market Fluctuations in the Colombo Stock Market: ARMA & PCA Approach”, Journal of Scientific Research & Reports 3(1): 130-138, 2014; Article no. JSRR,  .
    11. [11]     Wang Xiaohuaa, Dai Xiaqingb and Zhou Yuedongb, "Domestic energy consumption in rural China: A study on Sheyang County ofJiangsu Province",2009, Biomass and Bioenergy 22 : 251 – 256
    12. [12]     Liu Sifeng, Xie Naiming , Forrest, J, Bin Yu, "On a sort of new grey incidence models", Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on Digital Object Identifier: 10.1109/ICSMC.2008.4811525 Publication Year: 2008 , Page(s): 1652 - 1657.
    13. [13]     R.M. Kapila Tharanga  Rathnayaka and Zhong-jun Wang, “Influence of Family Status on the Dietary Patterns and Nutritional Levels of Children”, Food and Nutrition Sciences, 2013, .
    14. [14]     Wei Liu,"A Electronic Commerce Risk Evaluation Method Based on AHP and GRA ",Information Science and Engineering (ICISE), 2009 1st International Conference on Digital Object Identifier: 10.1109/ICISE.2009.26, Publication Year: 2009 , Page(s): 2791 - 2793 .
    15. [15]     Chow LC-h. A study of sectoral energy consumption in Hong Kong (1984–97) with special emphasis on the household sector. Energy Policy 2008;  29:1099–110.
    16. [16]     Ediger VS, Tatlldil H. Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Convers Manage 2002;43:473–87.
    17. [17]     He Y, Bao YD. Grey-Markov forecasting model and its application. Syst Eng 1992;9(4):59–63 [Theory Practice].
    18. [18]     R.M. Kapila Tharanga  Rathnayaka and Zhong-jun Wang, “Enhanced Greedy Optimization Algorithm with Data Warehousing for Automated Nurse Scheduling System”, E-Health Telecommunication Systems and Networks, 2013,
    19. [19]     Liu Na, Sun Wan-Hua. Research on Improving the Fitting and Prediction Precision of the GM(1,1) model [J].Mathematics in Practice and Theory,2009,38(4):33-39(in Chinese).
    20. [20]     Liu Bin, Zhao Liang, Zhai Zhen-Jie, Dang Yao-Guo.etc. Optimum Model of GM(1,1) and Its Suitable Range [J].Journal of NanJing University of Aeronaytics & Astronautics,2003,35(4):451-454(in Chinese)
    21. [21]     Chou CH.A variable structure controller based on the grey prediction technology[A].Proceedings of The American Control Conference[C].2001?1506(in English)
    22. [22]     Himanshu A. Amarawickrama and Lester C Hunt, "Electricity Demand for Sri Lanka: A Time Series Analysis", ISSN 1749-8384.
    23. [23]     R.M. Kapila Tharanga  Rathnayaka and P. M. Bandula Jayathileke, “Testing the Link between Inflation and Economic Growth: Evidence from Asia”, Communications and Network, 2013, .
    24. [24]     Rajaratnam Shanthini, "Could Sri Lanka afford sustainable electricity consumption practices without harming her economic growth?", Paper presented at the 9th Asia Pacific Roundtable for Sustainable Consumption and Production, June 2010.
    25. [25]     R.M.K.T Rathnayaka and Zhong-jun Wang, “Prevalence and effect of personal hygiene on transmission of helminthes infection among primary school children living in slums”, International Journal of Multidisciplinary Research Journal; ZENITH, ISSN: 2231-5780, Vol 02, April 2012.
    26. [26]     R.M Kapila Tharanga Rathnayaka, SC Nagahawatta and A.D. Naidu  “Electricity Demand For Sri Lanka: G M (1, 1) Forecasting Approach”, Wayamba international Conference (winc-2014), Wayaba University of Sri Lanka, Sri Lanka, (2104). .
    27. [27]     L. Ariyadasa, "Current Status of the Energy Situation in Sri Lanka", 2010.
    28. [28]     Xiping Wang, "Grey Prediction with Rolling Mechanism for Electricity Demand Forecasting of Shanghai", Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, November 18-20, 2007, Nanjing, China.