An improved unbiased grey model is developed to forecast urban gas supplies to efficiently allocate resources and ensure urban safety. The model uses three-point smoothing and an equal dimension new information model. Comparison of the improved unbiased grey model with the original model to predict the gas supplies indicates that the original model predictions decrease linearly so the differences between the predicted and actual gas supplies increase over time. The improved unbiased grey model gives nonlinear results that are better for mid-to-long term forecasts with the predicted gas supplies in better agreement with actual statistics. The average relative error for gas supplies predicted by the original model is 7.32% while that for the improved model is 5.76%.
杨杰, 翁文国. 基于改进无偏灰色模型的燃气供气量的预测[J]. 清华大学学报（自然科学版）, 2014, 54(2): 145-148.
Jie YANG, Wenguo WENG. Improved unbiased grey model for prediction of gas supplies. Journal of Tsinghua University(Science and Technology), 2014, 54(2): 145-148.
Beronich E L, Abedinzadegan A, Hawboldt K A. Prediction of natural gas behavior in loading and unloading operations of marine CNG transportation systems[J]. Journal of Natural Gas Science and Engineering, 2009, 1(1): 31-38.
Frota W M, Alberto J, Moraes S B. Natural gas: The option for a sustainable development and energy in the state of Amazonas[J]. Energy Policy, 2010, 38(7): 3830-3836.
Gorucu F B, Gumrah F. Evaluation and forecasting of gas consumption by statistical analysis[J]. Energy Sources, 2004, 26(3): 267-276.
Aras N. Forecasting residential consumption of natural gas using genetic algorithms[J]. Energy Exploration & Exploitation, 2008, 26(4): 241-266.
Gil S, Deferrari J. Generalized model of prediction of natural gas consumption[J]. Journal of Energy Resources Technology, 2004, 126(2): 90-98.
Suykens J, Lemmerling P, Favoreel W, et al.Modeling the Belgian gas consumption using neural networks[J]. Neural Processing Letters, 1996, 4(3): 157-166.
Azadeh A, Asadzadeh S M, Ghanhari A. An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments[J]. Energy Policy, 2010, 38(3): 1529-1536.
Wang Y F. Predicting stock price using fuzzy grey prediction system[J]. Expert Systems with Applications, 2002, 22(1): 33-39.
Tien T L. A research on the prediction of machining accuracy by the deterministic grey dynamic model DGDM(1, 1)[J]. Applied Mathematics and Computation, 2005, 161(3): 923-945.
Shih C S, Hsu Y T, Yeh J, et al.Grey number prediction using the grey modification model with progression technique[J]. Applied Mathematical Modeling, 2011, 35(3): 1314-1321.
Kayacan E, Ulutas B, Kaynak O. Grey system theory-based models in time series prediction[J]. Expert Systems with Applications, 2010, 37(2): 1784-1789.
Leephakpreeda T. Adaptive occupancy-based lighting control via grey prediction[J]. Building and Environment, 2005, 40(7): 881-886.
Akay D, Atak M. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey[J]. Energy, 2007, 32(9): 1670-1675.
朱芸, 乐秀璠. 可变参数无偏灰色模型的中长期负荷预测[J]. 电力自动化设备, 2003, 23(4): 25-27. ZHU Yun, LE Xiufan. Unbiased grey-forecasting model with unfixed parameter for long-term load[J]. Electric Power Automation Equipment, 2003, 23(4): 25-27. (in Chinese)
曾斌, 罗佑新. 新息与等维新息非等间距GM(1, 1) 模型及其应用[J]. 辽宁工程技术大学学报: 自然科学版, 2011, 30(4): 615-618. ZENG Bin, LUO Youxin. Unequal GM(1, 1) model of new information and equal dimension new information and its application[J]. Journal of Liaoning Technical University: Natural Science, 2011, 30(4): 615-618. (in Chinese)