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Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain

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  • Rehman, Aniqa
  • Zhu, Jun-Jie
  • Segovia, Javier
  • Anderson, Paul R.

Abstract

Prediction of natural gas demand can help to better manage energy demand and supply and recent developments of deep learning methods make it possible to improve forecast performance. This study examined the feasibility of hourly natural gas demand forecast, and compared statistical and deep learning methods to evaluate their prediction performance at five different sites in Spain. Hourly forecast can achieve an adjusted R2 ∼0.99 and MAPE lower to 2.7%. SMLR yields high prediction accuracy (MAPE: 3%–10%) in four sites, but suffers issues of missing data (5%–17%) and has relatively more extreme predictions (653 observations; ±100% away from the values). MLP has less amount of extreme predictions (517 observations) with a similar accuracy (MAPE: 3%–11%), but still suffers issue of missing data (11%–28%). LSTMs also achieves good prediction accuracy (MAPE: 3%–13%) and is able to manage most extreme values. Other methods are generally less promising but can be site-specific. Understanding their distinctive characteristics can help decision-makers to rule better decisions. Exploration of future forecasts based on LSTMs shows promising (adjusted R2: 0.90–0.99; MAPE: 11%–32%) in near future (<7 h), while model optimization can be used to further improve the performance, especially for a longer gap.

Suggested Citation

  • Rehman, Aniqa & Zhu, Jun-Jie & Segovia, Javier & Anderson, Paul R., 2022. "Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain," Energy, Elsevier, vol. 244(PA).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221028115
    DOI: 10.1016/j.energy.2021.122562
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    References listed on IDEAS

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    Cited by:

    1. Pala, Zeydin, 2023. "Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models," Energy, Elsevier, vol. 263(PC).
    2. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.

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