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An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model

Author

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  • Dengyong Zhang

    (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
    School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Haixin Tong

    (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
    School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Feng Li

    (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
    School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Lingyun Xiang

    (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
    School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
    Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha 410114, China)

  • Xiangling Ding

    (The School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411004, China)

Abstract

Ultra-short-term electrical load forecasting is an important guarantee for the safety and efficiency of energy system operation. Temperature is also an important factor affecting the changes in electric load. However, in different cases, the impact of temperature on load forecasting will vary greatly, and sometimes even lead to the decrease of forecasting accuracy. This often brings great difficulties to researchers’ work. In order to make more scientific use of temperature factor for ultra-short-term electrical load forecasting, especially to avoid the negative influence of temperature on load forecasting, in this paper we propose an ultra-short-term electrical load forecasting method based on temperature factor weight and long short-term memory model. The proposed method evaluates the importance of the current prediction task’s temperature based on the change magnitude of the recent load and the correlation between temperature and load, and therefore the negative impacts of the temperature model can be avoided. The mean absolute percentage error of proposed method is decreased by 1.24%, 1.86%, and 6.21% compared with traditional long short-term memory model, back-propagation neural network, and gray model on average, respectively. The experimental results demonstrate that this method has obvious advantages in prediction accuracy and generalization ability.

Suggested Citation

  • Dengyong Zhang & Haixin Tong & Feng Li & Lingyun Xiang & Xiangling Ding, 2020. "An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model," Energies, MDPI, vol. 13(18), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4875-:d:415087
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    References listed on IDEAS

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