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Short-term natural gas demand prediction based on support vector regression with false neighbours filtered

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  • Zhu, L.
  • Li, M.S.
  • Wu, Q.H.
  • Jiang, L.

Abstract

This paper presents a novel approach, named the SVR (support vector regression) based SVRLP (support vector regression local predictor) with FNF-SVRLP (false neighbours filtered-support vector regression local predictor), to predict short-term natural gas demand. This method integrates the SVR algorithm with the reconstruction properties of a time series, and optimises the original local predictor by removing false neighbours. A unified model, named the SM (“Standard Model”), is presented to process the entire dataset. To further improve the predicted accuracy, an AM (“Advanced Model”) is proposed, and is based on specific customer behaviours during different days of the week. The AM contains seven individual models for the seven days of the week. The FNF-SVRLP based AM has been used to predict natural gas demand for the National Grid of the United Kingdom (UK). This model outperforms the SVRLP, the ARMA (autoregressive moving average) and the ANN (artificial neural network) methods when applied to real-world data obtained from National Grid and has been successfully applied to daily gas operations for National Grid.

Suggested Citation

  • Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    • Handle: RePEc:eee:energy:v:80:y:2015:i:c:p:428-436
    DOI: 10.1016/j.energy.2014.11.083
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    1. Siemek, Jakub & Nagy, Stanislaw & Rychlicki, Stanislaw, 2003. "Estimation of natural-gas consumption in Poland based on the logistic-curve interpretation," Applied Energy, Elsevier, vol. 75(1-2), pages 1-7, May.
    2. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    3. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
    4. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    5. Wadud, Zia & Dey, Himadri S. & Kabir, Md. Ashfanoor & Khan, Shahidul I., 2011. "Modeling and forecasting natural gas demand in Bangladesh," Energy Policy, Elsevier, vol. 39(11), pages 7372-7380.
    6. de Almeida, Aníbal T. & Lopes, Ana Cristina & Carvalho, Anabela & Mariano, Jorge & Jahn, Andreas & Broege, Michael, 2004. "Examining the potential of natural gas demand-side measures to benefit customers, the distribution utility, and the environment: two case studies from Europe," Energy, Elsevier, vol. 29(7), pages 979-1000.
    7. Parikh, Jyoti & Purohit, Pallav & Maitra, Pallavi, 2007. "Demand projections of petroleum products and natural gas in India," Energy, Elsevier, vol. 32(10), pages 1825-1837.
    8. Gutiérrez, R. & Nafidi, A. & Gutiérrez Sánchez, R., 2005. "Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model," Applied Energy, Elsevier, vol. 80(2), pages 115-124, February.
    9. Taghavifar, Hamid & Mardani, Aref, 2014. "A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles," Energy, Elsevier, vol. 66(C), pages 569-576.
    10. Sarak, H & Satman, A, 2003. "The degree-day method to estimate the residential heating natural gas consumption in Turkey: a case study," Energy, Elsevier, vol. 28(9), pages 929-939.
    11. Salahshoor, Karim & Kordestani, Mojtaba & Khoshro, Majid S., 2010. "Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers," Energy, Elsevier, vol. 35(12), pages 5472-5482.
    12. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    13. Potocnik, Primoz & Thaler, Marko & Govekar, Edvard & Grabec, Igor & Poredos, Alojz, 2007. "Forecasting risks of natural gas consumption in Slovenia," Energy Policy, Elsevier, vol. 35(8), pages 4271-4282, August.
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