A Frequency Decomposition-Based Hybrid Forecasting Algorithm for Short-Term Reactive Power
This paper proposes a new frequency decomposition-based hybrid reactive power forecasting algorithm, EEMD-LSTM-RFR (ELR), which adopts a strategy of frequency decomposition prediction after ensemble empirical mode decomposition and then data reconstruction to improve the prediction ability of reacti...
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| Published in: | Energies (Basel) Vol. 14; no. 20; p. 6606 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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| ISSN: | 1996-1073, 1996-1073 |
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| Abstract | This paper proposes a new frequency decomposition-based hybrid reactive power forecasting algorithm, EEMD-LSTM-RFR (ELR), which adopts a strategy of frequency decomposition prediction after ensemble empirical mode decomposition and then data reconstruction to improve the prediction ability of reactive power. This decomposition process can compress the high frequency of reactive power and benefits the following separate forecasting. Long short-term memory is proposed for the high-frequency feature of reactive power to deal with the forecasting difficulty caused by strong signal disturbance and randomness. In contrast, random forest regression is applied to the low-frequency part in order to speed up the forecasting. Four classical algorithms and four hybrid algorithms based on different signal decompositions are compared with the proposed algorithm, and the results show that the proposed algorithm outperforms those algorithms. The predicting index RMSE decreases to 0.687, while the fitting degree R2 gradually approaches 1 with a step-by-step superposition of high-frequency signals, indicating that the proposed decomposition-predicting reconstruction strategy is effective. |
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| AbstractList | This paper proposes a new frequency decomposition-based hybrid reactive power forecasting algorithm, EEMD-LSTM-RFR (ELR), which adopts a strategy of frequency decomposition prediction after ensemble empirical mode decomposition and then data reconstruction to improve the prediction ability of reactive power. This decomposition process can compress the high frequency of reactive power and benefits the following separate forecasting. Long short-term memory is proposed for the high-frequency feature of reactive power to deal with the forecasting difficulty caused by strong signal disturbance and randomness. In contrast, random forest regression is applied to the low-frequency part in order to speed up the forecasting. Four classical algorithms and four hybrid algorithms based on different signal decompositions are compared with the proposed algorithm, and the results show that the proposed algorithm outperforms those algorithms. The predicting index RMSE decreases to 0.687, while the fitting degree R2 gradually approaches 1 with a step-by-step superposition of high-frequency signals, indicating that the proposed decomposition-predicting reconstruction strategy is effective. |
| Author | Yu, Jicheng Su, Tao Yue, Changxi Sun, Fan Shi, Ying Du, Jiabao Xie, Changjun |
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| Cites_doi | 10.23919/ACC.2018.8431220 10.1098/rspa.1998.0193 10.1103/PhysRevE.70.046217 10.1109/TSP.2017.2752720 10.3390/en13236241 10.1142/S1793536909000047 10.1109/TPWRS.2017.2669343 10.1016/j.ijepes.2017.07.007 10.1109/ITNEC48623.2020.9084912 10.1109/SSCI47803.2020.9308368 10.1016/j.apenergy.2019.01.055 10.1088/1755-1315/453/1/012056 10.1016/j.ijepes.2018.11.022 10.1109/TPWRS.2013.2283273 10.3390/en9100767 10.1016/j.powtec.2018.07.055 10.1109/ICIEA.2019.8833755 10.1134/S0005117911070083 10.1109/PTC.2015.7232530 10.1109/ACCESS.2020.2983588 10.1162/neco.1997.9.8.1735 10.1109/TSG.2012.2231441 10.1016/j.physa.2018.09.120 10.1080/01621459.1963.10500845 10.1098/rspa.2003.1221 10.1007/s40565-013-0016-0 10.1109/72.788646 |
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| References | Huang (ref_20) 1998; 454 Bracale (ref_5) 2013; 4 Peyman (ref_31) 2018; 338 Wang (ref_36) 2020; 453 ref_14 ref_35 ref_34 ref_33 ref_32 Wu (ref_24) 2019; 516 Tao (ref_9) 2018; 39 Wu (ref_22) 2009; 1 ref_19 Bracale (ref_6) 2019; 107 ref_17 Kim (ref_3) 2015; 8 Ward (ref_29) 1963; 58 ref_15 Wu (ref_12) 2015; 39 Cao (ref_30) 2004; 70 Xu (ref_2) 2017; 32 Qin (ref_7) 2013; 1 Nie (ref_11) 2017; 55 Peng (ref_10) 2018; 39 He (ref_23) 2019; 237 Kurbatskii (ref_21) 2011; 72 Hochreiter (ref_16) 1997; 9 Shen (ref_4) 2018; 94 Ren (ref_8) 2020; 44 Wu (ref_27) 2004; 460 ref_1 Chapelle (ref_13) 1999; 10 Wen (ref_18) 2014; 29 ref_26 Harmouche (ref_28) 2018; 66 Yang (ref_25) 2020; 8 |
| References_xml | – ident: ref_26 doi: 10.23919/ACC.2018.8431220 – volume: 454 start-page: 903 year: 1998 ident: ref_20 article-title: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis publication-title: Proc. Math. Phys. Eng. Sci. doi: 10.1098/rspa.1998.0193 – volume: 70 start-page: 046217 year: 2004 ident: ref_30 article-title: Detecting dynamical changes in time series using the permutation entropy publication-title: Phys. Rev. E Stat. Nonlin Soft Matter Phys. doi: 10.1103/PhysRevE.70.046217 – volume: 66 start-page: 251 year: 2018 ident: ref_28 article-title: The Sliding Singular Spectrum Analysis: A Data-Driven Nonstationary Signal Decomposition Tool publication-title: IEEE Trans. Signal. Process. doi: 10.1109/TSP.2017.2752720 – ident: ref_35 doi: 10.3390/en13236241 – volume: 55 start-page: 390 year: 2017 ident: ref_11 article-title: Power load prediction based on multiple linear regression model publication-title: Bol. Tec. Tech. Bull. – volume: 1 start-page: 1 year: 2009 ident: ref_22 article-title: Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method publication-title: Adv. Adapt. Data Anal. doi: 10.1142/S1793536909000047 – volume: 32 start-page: 4398 year: 2017 ident: ref_2 article-title: Multi-Timescale Coordinated Voltage/Var Control of High Renewable-Penetrated Distribution Systems publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2017.2669343 – volume: 94 start-page: 287 year: 2018 ident: ref_4 article-title: Three-stage power system restoration methodology considering renewable energies publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2017.07.007 – ident: ref_14 doi: 10.1109/ITNEC48623.2020.9084912 – ident: ref_34 doi: 10.1109/SSCI47803.2020.9308368 – volume: 237 start-page: 103 year: 2019 ident: ref_23 article-title: 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 publication-title: Appl. Energy doi: 10.1016/j.apenergy.2019.01.055 – volume: 453 start-page: 012056 year: 2020 ident: ref_36 article-title: The Power of Short-term Load Algorithm Based on LSTM publication-title: IOP Conf. Ser. Earth Environ. Sci. doi: 10.1088/1755-1315/453/1/012056 – volume: 107 start-page: 177 year: 2019 ident: ref_6 article-title: Short-term industrial reactive power forecasting publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2018.11.022 – volume: 29 start-page: 765 year: 2014 ident: ref_18 article-title: Frequency estimation of distorted and noisy signals in power systems by fft-based approach publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2013.2283273 – ident: ref_33 doi: 10.3390/en9100767 – volume: 338 start-page: 983 year: 2018 ident: ref_31 article-title: Random Forest regression prediction of solid particle Erosion in elbows publication-title: Powder Technol. doi: 10.1016/j.powtec.2018.07.055 – ident: ref_17 doi: 10.1109/ICIEA.2019.8833755 – volume: 8 start-page: 515 year: 2015 ident: ref_3 article-title: Reactive Power Ancillary Service of Synchronous DGs in Coordination with Voltage Control Devices publication-title: IEEE Trans. Smart Grid – volume: 72 start-page: 1405 year: 2011 ident: ref_21 article-title: On the neural network approach for forecasting of nonstationary time series on the basis of the hilbert-huang transform publication-title: Autom. Remote Control doi: 10.1134/S0005117911070083 – ident: ref_32 doi: 10.1109/PTC.2015.7232530 – volume: 8 start-page: 61915 year: 2020 ident: ref_25 article-title: Hybrid Method for Short-Term Time Series Forecasting Based on EEMD publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2983588 – volume: 39 start-page: 116 year: 2018 ident: ref_9 article-title: Selecting and Optimization Method of Reactive Power Compensation Node in Power System publication-title: Power Capacit. React. Power Compens. – volume: 9 start-page: 1735 year: 1997 ident: ref_16 article-title: Long Short-Term Memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 44 start-page: 1463 year: 2020 ident: ref_8 article-title: Reactive power optimization planning of high-voltage distribution networks with technical standards being considered publication-title: Power Syst. Technol. – volume: 4 start-page: 1760 year: 2013 ident: ref_5 article-title: A Bayesian-Based Approach for a Short-Term Steady-State Forecast of a Smart Grid publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2012.2231441 – volume: 39 start-page: 1 year: 2018 ident: ref_10 article-title: Practical and Simplified Calculation Method and Case Analysis on Reactive Power Balance Allocation for 500 kV Substation publication-title: Power Capacit. React. Power Compens. – ident: ref_1 doi: 10.23919/ACC.2018.8431220 – ident: ref_15 – volume: 516 start-page: 114 year: 2019 ident: ref_24 article-title: Improved eemd-based crude oil price forecasting using lstm networks publication-title: Phys. A Stat. Mech. Its Appl. doi: 10.1016/j.physa.2018.09.120 – volume: 58 start-page: 236 year: 1963 ident: ref_29 article-title: Hierarchical grouping to optimize an objective function publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1963.10500845 – ident: ref_19 – volume: 460 start-page: 1597 year: 2004 ident: ref_27 article-title: A study of the characteristics of white noise using the empirical mode decomposition method publication-title: Proc. Math. Phys. Eng. Sci. doi: 10.1098/rspa.2003.1221 – volume: 39 start-page: 50 year: 2015 ident: ref_12 article-title: Power System Short-term Load Forecasting Based on Improved Random Forest with Grey Relation Projection publication-title: Autom. Electr. Power Syst. – volume: 1 start-page: 150 year: 2013 ident: ref_7 article-title: Risk analysis of power systems for both real and reactive power publication-title: J. Mod. Power Syst. Clean Energy doi: 10.1007/s40565-013-0016-0 – volume: 10 start-page: 1055 year: 1999 ident: ref_13 article-title: Support vector machines for histogram-based image classification publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.788646 |
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| SubjectTerms | Accuracy Algorithms Decomposition Energy resources ensemble empirical mode decomposition Forecasting forecasting algorithm long short-term memory Machine learning Neural networks Noise random forest regression reactive power Signal processing Time series Wavelet transforms |
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| Title | A Frequency Decomposition-Based Hybrid Forecasting Algorithm for Short-Term Reactive Power |
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