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Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs

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  • Mansoor, Muhammad
  • Grimaccia, Francesco
  • Leva, Sonia
  • Mussetta, Marco

Abstract

The electrical load forecasting is a fundamental technique for consumer load prediction for utilities. The accurate load forecasting is crucial to design Demand Response (DR) programs in the paradigm of smart grids. Artificial Neural Network (ANN) based techniques have been widely used in recent years and applied to predict the electric load with high accuracy to participate in DR programs for commercial, industrial and residential consumers. This research work is focused on the use and comparison of two ANN-based load forecasting techniques, i.e. Feed-Forward Neural Network (FFNN) and Echo State Network (ESN), on a dataset related to commercial buildings, in view of a possible DR program application. The results of both models are compared based on the load forecasting accuracy through experimental measurements and suitably defined metrics.

Suggested Citation

  • Mansoor, Muhammad & Grimaccia, Francesco & Leva, Sonia & Mussetta, Marco, 2021. "Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 282-293.
    • Handle: RePEc:eee:matcom:v:184:y:2021:i:c:p:282-293
    DOI: 10.1016/j.matcom.2020.07.011
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    Cited by:

    1. Venkataramana Veeramsetty & Dongari Rakesh Chandra & Francesco Grimaccia & Marco Mussetta, 2022. "Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks," Forecasting, MDPI, vol. 4(1), pages 1-16, January.
    2. Delagnes, T. & Henneron, T. & Clenet, S. & Fratila, M. & Ducreux, J.P., 2023. "Comparison of reduced basis construction methods for Model Order Reduction, with application to non-linear low frequency electromagnetics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 211(C), pages 470-488.
    3. Carla Sahori Seefoo Jarquin & Alessandro Gandelli & Francesco Grimaccia & Marco Mussetta, 2023. "Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks," Forecasting, MDPI, vol. 5(2), pages 1-15, April.
    4. 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.
    5. Pedro M. R. Bento & Jose A. N. Pombo & Maria R. A. Calado & Silvio J. P. S. Mariano, 2021. "Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting," Energies, MDPI, vol. 14(21), pages 1-21, November.
    6. Vidya Krishnan Mololoth & Saguna Saguna & Christer Åhlund, 2023. "Blockchain and Machine Learning for Future Smart Grids: A Review," Energies, MDPI, vol. 16(1), pages 1-39, January.

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