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Real-Time Pricing-Enabled Demand Response Using Long Short-Time Memory Deep Learning

Author

Listed:
  • Aftab Ahmed Almani

    (Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jingshi Road 17923, Jinan 250000, China
    These authors contributed equally to this work.)

  • Xueshan Han

    (Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jingshi Road 17923, Jinan 250000, China
    These authors contributed equally to this work.)

Abstract

Sustainable energy development requires environment-friendly energy-generating methods. Pricing system constraints influence the efficient use of energy resources. Real-Time Pricing (RTP) is theoretically superior to previous pricing systems for allowing demand response (DR) activities. The DR approach has been useful for correcting supply–demand imbalances as technology has evolved. There are several systems for determining and controlling the DR. However, most of these solutions are unable to control rising demand or forecast prices for future time slots. This research provides a Real-Time Pricing DR model for energy management based on deep learning, where the learning framework is trained on demand response and real-time pricing. The study data in this article were taken from the Australian Energy Market Operator (AEMO), and the learning framework was trained over 17 years of data to forecast the real future energy price and demand. To investigate the suggested deep learning-based dynamic pricing strategy, two prediction instances are addressed: actual–predicted demand and actual–predicted price. We estimated pricing and demand outcomes using long short-term memory (LSTM), which were then greatly improved by architectural changes in the model. The findings showed that the suggested model is suitable for energy management in terms of demand and price prediction.

Suggested Citation

  • Aftab Ahmed Almani & Xueshan Han, 2023. "Real-Time Pricing-Enabled Demand Response Using Long Short-Time Memory Deep Learning," Energies, MDPI, vol. 16(5), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2410-:d:1086306
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    References listed on IDEAS

    as
    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Wang, Jianzhou & Zhu, Wenjin & Zhang, Wenyu & Sun, Donghuai, 2009. "A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand," Energy Policy, Elsevier, vol. 37(11), pages 4901-4909, November.
    3. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    4. Aghaei, Jamshid & Alizadeh, Mohammad-Iman & Siano, Pierluigi & Heidari, Alireza, 2016. "Contribution of emergency demand response programs in power system reliability," Energy, Elsevier, vol. 103(C), pages 688-696.
    5. Shen, Bo & Ghatikar, Girish & Lei, Zeng & Li, Jinkai & Wikler, Greg & Martin, Phil, 2014. "The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges," Applied Energy, Elsevier, vol. 130(C), pages 814-823.
    6. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
    7. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    8. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    9. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    10. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    11. Miller, Mark & Alberini, Anna, 2016. "Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: Evidence from U.S. Data," Energy Policy, Elsevier, vol. 97(C), pages 235-249.
    12. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
    13. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    14. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    15. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
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