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Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting

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  • Lintao Yang

    (College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

  • Honggeng Yang

    (College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

Abstract

Short-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but without a systematic comparative analysis. In this paper, we first compare the most frequently used neural networks’ performance on the load dataset from the State Grid Sichuan Electric Power Company (China). Then, considering the current neural networks’ disadvantages, we propose a new architecture called a gate-recurrent neural network (RNN) based on an RNN for STLF. By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, particularly when the time scale is smaller than 20 min.

Suggested Citation

  • Lintao Yang & Honggeng Yang, 2019. "Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1433-:d:222669
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    References listed on IDEAS

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    Cited by:

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    2. Jose R. Cedeño González & Juan J. Flores & Claudio R. Fuerte-Esquivel & Boris A. Moreno-Alcaide, 2020. "Nearest Neighbors Time Series Forecaster Based on Phase Space Reconstruction for Short-Term Load Forecasting," Energies, MDPI, vol. 13(20), pages 1-24, October.
    3. Heung-gu Son & Yunsun Kim & Sahm Kim, 2020. "Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid," Energies, MDPI, vol. 13(9), pages 1-14, May.
    4. Bibi Ibrahim & Luis Rabelo & Edgar Gutierrez-Franco & Nicolas Clavijo-Buritica, 2022. "Machine Learning for Short-Term Load Forecasting in Smart Grids," Energies, MDPI, vol. 15(21), pages 1-19, October.

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