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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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    1. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    2. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    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. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    5. Hao, Xiaochen & Guo, Tongtong & Huang, Gaolu & Shi, Xin & Zhao, Yantao & Yang, Yue, 2020. "Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window," Energy, Elsevier, vol. 207(C).
    6. Forouzanfar, Mehdi & Doustmohammadi, Ali & Menhaj, M. Bagher & Hasanzadeh, Samira, 2010. "Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran," Applied Energy, Elsevier, vol. 87(1), pages 268-274, January.
    7. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    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. Mustafa Akpinar & M. Fatih Adak & Nejat Yumusak, 2017. "Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey," Energies, MDPI, vol. 10(6), pages 1-20, June.
    Full references (including those not matched with items on IDEAS)

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    3. 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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