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Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network

Author

Listed:
  • Shaoqian Pei

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Hui Qin

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Liqiang Yao

    (Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan 430074, China)

  • Yongqi Liu

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chao Wang

    (Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100044, China)

  • Jianzhong Zhou

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. However, how to select the optimal feature set and accurately predict multi-step ahead short-term load still faces huge challenges. In this paper, a hybrid feature selection method is proposed, an Improved Long Short-Term Memory network (ILSTM) is applied to predict multi-step ahead load. This method firstly takes the influence of temperature, humidity, dew point, and date type on the load into consideration. Furthermore, the maximum information coefficient is used for the preliminary screening of historical load, and Max-Relevance and Min-Redundancy (mRMR) is employed for further feature selection. Finally, the selected feature set is considered as input of the model to perform multi-step ahead short-term load prediction by the Improved Long Short-Term Memory network. In order to verify the performance of the proposed model, two categories of contrast methods are applied: (1) comparing the model with hybrid feature selection and the model which does not adopt hybrid feature selection; (2) comparing different models including Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) using hybrid feature selection. The result of the experiments, which were developed during four periods in the Hubei Province, China, show that hybrid feature selection can improve the prediction accuracy of the model, and the proposed model can accurately predict the multi-step ahead load.

Suggested Citation

  • Shaoqian Pei & Hui Qin & Liqiang Yao & Yongqi Liu & Chao Wang & Jianzhong Zhou, 2020. "Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network," Energies, MDPI, vol. 13(16), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4121-:d:396734
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    References listed on IDEAS

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    Cited by:

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    2. Ankit Kumar Srivastava & Ajay Shekhar Pandey & Mohamad Abou Houran & Varun Kumar & Dinesh Kumar & Saurabh Mani Tripathi & Sivasankar Gangatharan & Rajvikram Madurai Elavarasan, 2023. "A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection," Energies, MDPI, vol. 16(2), pages 1-23, January.
    3. Xiyong Zhao & Yanzhou Li & Yongli Chen & Xi Qiao, 2022. "A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images," Sustainability, MDPI, vol. 14(19), pages 1-15, October.
    4. Yamin Shen & Yuxuan Ma & Simin Deng & Chiou-Jye Huang & Ping-Huan Kuo, 2021. "An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    5. Mehmet Türker Takcı & Tuba Gözel, 2022. "Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    6. Xiaocong Xiao & Jianxun Liu & Deshun Liu & Yufei Tang & Shigang Qin & Fan Zhang, 2022. "A Normal Behavior-Based Condition Monitoring Method for Wind Turbine Main Bearing Using Dual Attention Mechanism and Bi-LSTM," Energies, MDPI, vol. 15(22), pages 1-17, November.
    7. Jicheng Liu & Yu Yin, 2022. "Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China," Energies, MDPI, vol. 15(3), pages 1-23, February.

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