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Artificial Neural Network Model for Hourly Peak Load Forecast

Author

Listed:
  • V. Ramesh Kumar

    (Department of Electrical and Electronics Engineering, School of Engineering and Technology, Jain University, Jain Global Campus, Bengaluru - 562 112, Karnataka, India,)

  • Pradipkumar Dixit

    (Department of Electrical and Electronics Engineering, M. S. Ramaiah Institute of Technology, Bengaluru - 560 054, Karnataka, India. *)

Abstract

Artificial Neural Network model for short-term demand forecast of hourly peak load is proposed in this paper. For learning of the ANN model Levenberg-Marquardt algorithm is adopted because of its ability to handle the large number of non-linear load data. The training of network is done by using hourly peak load data of preceding five years from the period of forecast and the temperature data. The validation of the developed ANN model is tested with historical load data of BESCOM (Bangalore Electricity Supply Company Limited) power system. The comparison of conventional methods and ANN model with respect to percentage error is evaluated, from the results it has been found that the proposed ANN model with optimal number of hidden layer neurons gives accurate predictions.

Suggested Citation

  • V. Ramesh Kumar & Pradipkumar Dixit, 2018. "Artificial Neural Network Model for Hourly Peak Load Forecast," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 155-160.
    • Handle: RePEc:eco:journ2:2018-05-20
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    Citations

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    Cited by:

    1. Lee, Juyong & Cho, Youngsang, 2022. "Determinants of reserve margin volatility: A new approach toward managing energy supply and demand," Energy, Elsevier, vol. 252(C).
    2. Ademola Abdulkareem & E. J. Okoroafor & Ayokunle Awelewa & Aderibigbe Adekitan, 2019. "Pseudo-Inverse Matrix Model for Estimating Long-Term Annual Peak Electricity Demand: The Covenant University s Experience," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 103-109.
    3. Kaneko, Nanae & Fujimoto, Yu & Kabe, Satoshi & Hayashida, Motonari & Hayashi, Yasuhiro, 2020. "Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand," Applied Energy, Elsevier, vol. 265(C).

    More about this item

    Keywords

    Artificial Neural Network; Normalization; Forecasting;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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