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Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting

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  • Yuanhang Qi

    (School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China
    School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Haoyu Luo

    (School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China
    School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Yuhui Luo

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Rixu Liao

    (School of Accountancy, Guangdong Baiyun University, Guangzhou 510550, China)

  • Liwei Ye

    (School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China)

Abstract

Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed to effectively combine the clustering process and prediction process. More specifically, the clustering process adopts the maximum deviation similarity criterion clustering algorithm (MDSC) as the clustering framework. A bee-foraging learning particle swarm optimization is further applied to realize the adaptive optimization of its hyperparameters. The prediction process consists of three parts: (i) a 9-dimensional load feature vector is proposed as the classification feature of SVM to obtain the load similarity cluster of the predicted days; (ii) the same kind of data are used as the training data of long short-term memory network; (iii) the trained network is used to predict the power load curve of the predicted day. Finally, experimental results are presented to show that the proposed scheme achieves an advantage in the prediction accuracy, where the mean absolute percentage error between predicted value and real value is only 8.05% for the first day.

Suggested Citation

  • Yuanhang Qi & Haoyu Luo & Yuhui Luo & Rixu Liao & Liwei Ye, 2023. "Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting," Energies, MDPI, vol. 16(17), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6230-:d:1226684
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    References listed on IDEAS

    as
    1. He, Feifei & Zhou, Jianzhong & Feng, Zhong-kai & Liu, Guangbiao & Yang, Yuqi, 2019. "A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm," Applied Energy, Elsevier, vol. 237(C), pages 103-116.
    2. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Reis, Agnaldo J.R. & Enayatifar, Rasul & Souza, Marcone J.F. & Guimarães, Frederico G., 2016. "A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment," Applied Energy, Elsevier, vol. 169(C), pages 567-584.
    3. Erdogdu, Erkan, 2007. "Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey," Energy Policy, Elsevier, vol. 35(2), pages 1129-1146, February.
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