Study of the Load Forecasting based on AKDC and LSTM algorithms
As one of the traditional research subjects of power system, load forecasting has always been a hot research direction of related experts and scholars. This paper uses an extended algorithm combining the advantages of adaptive K-means algorithm and distributed clustering algorithm, improves the trad...
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| Published in | Journal of physics. Conference series Vol. 2589; no. 1; pp. 12035 - 12041 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bristol
IOP Publishing
01.09.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1742-6588 1742-6596 1742-6596 |
| DOI | 10.1088/1742-6596/2589/1/012035 |
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| Abstract | As one of the traditional research subjects of power system, load forecasting has always been a hot research direction of related experts and scholars. This paper uses an extended algorithm combining the advantages of adaptive K-means algorithm and distributed clustering algorithm, improves the traditional K-means algorithm, and uses LSTM algorithm to build a load prediction model. LSTMS can learn the advantages of long distance time series dependence to recognize load patterns from the horizontal (time dimension). The simulation results show that the LSTM algorithm based on Adam optimizer improves the accuracy of load prediction, and the proposed algorithm is verified. |
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| AbstractList | As one of the traditional research subjects of power system, load forecasting has always been a hot research direction of related experts and scholars. This paper uses an extended algorithm combining the advantages of adaptive K-means algorithm and distributed clustering algorithm, improves the traditional K-means algorithm, and uses LSTM algorithm to build a load prediction model. LSTMS can learn the advantages of long distance time series dependence to recognize load patterns from the horizontal (time dimension). The simulation results show that the LSTM algorithm based on Adam optimizer improves the accuracy of load prediction, and the proposed algorithm is verified. |
| Author | Zhou, Xinhui Wang, Kang Ding, Mingming Wang, Zhenshu |
| Author_xml | – sequence: 1 givenname: Mingming surname: Ding fullname: Ding, Mingming organization: School of Electrical Engineering, Shandong University , China – sequence: 2 givenname: Zhenshu surname: Wang fullname: Wang, Zhenshu organization: School of Electrical Engineering, Shandong University , China – sequence: 3 givenname: Xinhui surname: Zhou fullname: Zhou, Xinhui organization: School of Electrical Engineering, Shandong University , China – sequence: 4 givenname: Kang surname: Wang fullname: Wang, Kang organization: School of Electrical Engineering, Shandong University , China |
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| References | Wei (JPCS_2589_1_012035bib4) 2021; 33 H. U M M, X. L. (JPCS_2589_1_012035bib12) 2019[C] Chen (JPCS_2589_1_012035bib5) 2018; 36 Liqin (JPCS_2589_1_012035bib8) 2022; 2378 Gong (JPCS_2589_1_012035bib1) 2021; 41 Chen (JPCS_2589_1_012035bib3) 2020; 44 (JPCS_2589_1_012035bib6) 1999 Wei (JPCS_2589_1_012035bib9) 2022; 2166 H. M, F. S (JPCS_2589_1_012035bib11) 1999 Peiqiang (JPCS_2589_1_012035bib10) 2005; 25 Jixiang (JPCS_2589_1_012035bib7) 2019; 0 Pang (JPCS_2589_1_012035bib2) 2021; 40 |
| References_xml | – year: 1999 ident: JPCS_2589_1_012035bib11 article-title: Daily load curve clustering and prediction by neural model tool box for power systems with non-stochastic load components publication-title: 1999 European Control Conference (ECC), 1999[C] – volume: 40 start-page: 175 year: 2021 ident: JPCS_2589_1_012035bib2 article-title: Short-term power load forecasting based on LSTM recurrent neural network publication-title: Electric Power Engineering Technology – volume: 0 start-page: 131 year: 2019 ident: JPCS_2589_1_012035bib7 article-title: Short-term Load Forecasting Method Based on CNN-LSTM Hybrid Neural Network Model[J] publication-title: Automation of Electric Power Systems – year: 1999 ident: JPCS_2589_1_012035bib6 – volume: 44 start-page: 614 year: 2020 ident: JPCS_2589_1_012035bib3 article-title: Ultra Short-term Power Load Forecasting Based on Combined LSTM-XGBoost Model publication-title: Power System Technology – volume: 41 start-page: 81 year: 2021 ident: JPCS_2589_1_012035bib1 article-title: Short-term power load forecasting method based on Attention-BiLSTM-LSTM neural network publication-title: journal of Computer Applications – volume: 25 start-page: 73 year: 2005 ident: JPCS_2589_1_012035bib10 article-title: The Characteristics Classification and Synthesis of Power Load Based on Fuzzy Clustering[J] publication-title: Proceedings of the Csee – volume: 33 start-page: 1866 year: 2021 ident: JPCS_2589_1_012035bib4 article-title: Short-term Power Load Forecasting Based on LSTM Neural Network Optimized by Improved PSO publication-title: in Journal of System Simulation – volume: 2378 year: 2022 ident: JPCS_2589_1_012035bib8 article-title: Analysis of Power Load Characteristics Based on Adaptive Ensemble Clustering Algorithm[J] publication-title: Journal of Physics: Conference Series – year: 2019[C] ident: JPCS_2589_1_012035bib12 article-title: Composite Load Model and Transfer Function Based Load Model for High Motor Composition Load publication-title: 2019 IEEE Electrical Power and Energy Conference (EPEC) – volume: 36 start-page: 39 year: 2018 ident: JPCS_2589_1_012035bib5 article-title: Short-Term Electrical Load Forecasting Based on Deep Learning LSTM Networks publication-title: in Electronics Design&Application – volume: 2166 year: 2022 ident: JPCS_2589_1_012035bib9 article-title: Research on Load Characteristic Data Clustering Based on Transfer Learning[J] publication-title: Journal of Physics: Conference Series |
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| SubjectTerms | Adaptive algorithms Clustering Forecasting Pattern recognition Physics Prediction models Time dependence |
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| Title | Study of the Load Forecasting based on AKDC and LSTM algorithms |
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