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A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting

Author

Listed:
  • Aoqi Xu

    (School of Economics, Fujian Normal University, Fuzhou 350007, China)

  • Man-Wen Tian

    (National Key Project Laboratory, Jiangxi University of Engineering, Xinyu 338000, China)

  • Behnam Firouzi

    (Vibrations and Acoustics Laboratory (VAL), Mechanical Engineering Department, Ozyegin University, Istanbul 34794, Turkey)

  • Khalid A. Alattas

    (Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia)

  • Ardashir Mohammadzadeh

    (Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Ebrahim Ghaderpour

    (Department of Earth Sciences, Sapienza University of Rome, Piazzale Aldo-Moro, 5, 00185 Rome, Italy)

Abstract

A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning the parameters. All parameters of RBMs, the number of input variables, the type of inputs, and also the layer and neuron numbers are optimized. A statistical approach is suggested to determine the effective input variables. In addition to the climate variables, such as temperature and humidity, the effects of other variables such as economic factors are also investigated. Finally, using simulated and real-world data examples, it is shown that for one year ahead, the mean absolute percentage error (MAPE) for the load peak is less than 5%. Moreover, for the 24-h pattern forecasting, the mean of MAPE for all days is less than 5%.

Suggested Citation

  • Aoqi Xu & Man-Wen Tian & Behnam Firouzi & Khalid A. Alattas & Ardashir Mohammadzadeh & Ebrahim Ghaderpour, 2022. "A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting," Sustainability, MDPI, vol. 14(16), pages 1-12, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10081-:d:888301
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    References listed on IDEAS

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    Cited by:

    1. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    2. Shivam Swarup & Gyaneshwar Singh Kushwaha, 2022. "Effects of Temperature Rise on Clean Energy-Based Capital Market Investments: Neural Network-Based Granger Causality Analysis," Sustainability, MDPI, vol. 14(18), pages 1-12, September.
    3. Yijun Wang & Peiqian Guo & Nan Ma & Guowei Liu, 2022. "Robust Wavelet Transform Neural-Network-Based Short-Term Load Forecasting for Power Distribution Networks," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
    4. Paweł Pełka, 2023. "Analysis and Forecasting of Monthly Electricity Demand Time Series Using Pattern-Based Statistical Methods," Energies, MDPI, vol. 16(2), pages 1-22, January.

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