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An artificial neural network-based forecasting model of energy-related time series for electrical grid management

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  • Di Piazza, A.
  • Di Piazza, M.C.
  • La Tona, G.
  • Luna, M.

Abstract

Forecasting of energy-related variables is crucial for accurate planning and management of electrical power grids, aiming at improving overall efficiency and performance. In this paper, an artificial neural network (ANN)-based model is investigated for short-term forecasting of the hourly wind speed, solar radiation, and electrical power demand. Specifically, the non-linear autoregressive network with exogenous inputs (NARX) ANN is considered, compared to other models, and then selected to perform multi-step-ahead forecasting. Different time horizons have been considered in the range between 8 and 24 h ahead. The simulation analysis has put in evidence the main advantage of the proposed method, i.e., its capability to reconcile good forecasting performance in the short-term time horizon with a very simple network structure, which is potentially implementable on a low-cost processing platform.

Suggested Citation

  • Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
    • Handle: RePEc:eee:matcom:v:184:y:2021:i:c:p:294-305
    DOI: 10.1016/j.matcom.2020.05.010
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    2. Salam, Abdulwahed & El Hibaoui, Abdelaaziz, 2021. "Energy consumption prediction model with deep inception residual network inspiration and LSTM," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 97-109.
    3. Giuseppe La Tona & Maria Carmela Di Piazza & Massimiliano Luna, 2021. "Effect of Daily Forecasting Frequency on Rolling-Horizon-Based EMS Reducing Electrical Demand Uncertainty in Microgrids," Energies, MDPI, vol. 14(6), pages 1-16, March.
    4. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    5. N. Yogambal Jayalakshmi & R. Shankar & Umashankar Subramaniam & I. Baranilingesan & Alagar Karthick & Balasubramaniam Stalin & Robbi Rahim & Aritra Ghosh, 2021. "Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting," Energies, MDPI, vol. 14(9), pages 1-23, April.
    6. Simos, Theodore E. & Katsikis, Vasilios N. & Mourtas, Spyridon D., 2022. "Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 451-465.
    7. Malak Adnan Khan & Qudrat Khan & Laiq Khan & Imran Khan & Ahmad Aziz Alahmadi & Nasim Ullah, 2022. "Robust Differentiator-Based NeuroFuzzy Sliding Mode Control Strategies for PMSG-WECS," Energies, MDPI, vol. 15(19), pages 1-18, September.
    8. Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.
    9. Asad Ali & Muhammad Salman Fakhar & Syed Abdul Rahman Kashif & Ghulam Abbas & Irfan Ahmad Khan & Akhtar Rasool & Nasim Ullah, 2022. "Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid," Energies, MDPI, vol. 15(23), pages 1-31, November.
    10. Massimiliano Luna & Giuseppe La Tona & Angelo Accetta & Marcello Pucci & Andrea Pietra & Maria Carmela Di Piazza, 2023. "Optimal Management of Battery and Fuel Cell-Based Decentralized Generation in DC Shipboard Microgrids," Energies, MDPI, vol. 16(4), pages 1-21, February.

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