Yingdong Lu
ABSTRACT
Ensuring the stability of power systems is an important issue that should be considered in order to ensure the social and economic development of a country. Therefore, predicting the chaotic behavior of power systems in order to develop protection measures and keep power systems stable is vital. In this paper, a deep learning algorithm was proposed to predict the chaotic behavior of power systems by using deep long short-term memory (DLSTM) networks, which have two forms: deep long short-term memory with static scenario (DLSTM-s) and deep long-term memory with dynamic scenario (DLSTM-d). The genetic algorithm was used to optimize the hyperparameters of the networks. Then, taking interconnected power systems as an example, the effectiveness of the proposed DLSTM network was verified via numerical simulation. Finally, the experimental results of the DLSTM network were compared with those of the echo state network, multi-recurrent neural network, deep gated recurrent unit, and long short-term memory. Experimental results illustrated that a trained DLSTM network can predict the chaotic behavior of power systems by using the time series data of a single state variable. Moreover, the DLSTM-s network proposed in this paper can achieve competitive prediction performance compared with other baseline methods.
- Mao Y , Tian-Feng L , Ben-Ming J I . A Review of Chaos Theory in Power System Load Prediction [J]. Journal of Northeast Dianli University, 2015, 35(03): 18–21Google Scholar
- Chao F, Yongjun L, Liang T, Experiment and Analysis on Asynchronously Interconnected System of Yunnan Power Grid and Main Grid of China Southern Power Grid [J]. Southern Power System Technology, 2016, 10(07): 1–5+12Google Scholar
- Xiaoda H, Hao Y, Hao F. Lyapunov-based event-triggered control for nonlinear plants subject to disturbances and transmission delays [J]. Chin. Phys, 2020, 063(005):142–156Google Scholar
- Huang X, Ye G. An efficient self-adaptive model for chaotic image encryption algorithm [J]. Communications in Nonlinear Science and Numerical Simulation, 2014, 19(12): 4094–4104Google ScholarCross Ref
- Li W K. On a mixture autoregressive model [J]. Journal of the Royal Statal Society, 2010, 62(455): 95–115Google Scholar
- Lu J , Zhou J , Wang H , An approach to structural approximation analysis by artificial neural networks [J]. Science in China Series A-Mathematics, Physics, Astronomy & Technological Science. A, 1994, 37(8): 990–997Google Scholar
- Schmidhuber J, Deep Learning in Neural Networks: An Overview [J]. Neural Networks, 2015, 61:85–117Google ScholarDigital Library
- Soltanolkotabi M, Javanmard A, Lee J D. Theoretical insights into the optimization landscape of over-parameterized shallow neural networks [J]. IEEE Transactions on Information Theory, 2017, 65(2): 742–769Google ScholarDigital Library
- Yildirim, O. A novel wavelet sequences based on deep bidirectional LSTM network model for ECG signal classification [J]. Computers in Biology & Medicine, 2018, 96:189–202Google ScholarCross Ref
- Sagheer A, Kotb M. Time Series Forecasting of Petroleum Production using Deep LSTM Recurrent Networks [J]. Neurocomputing, 2019, 323:203–213Google ScholarCross Ref
- Abdel-Nasser M, Mahmoud K, Lehtonen M. Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs [J]. IEEE Transactions on Industrial Informatics, 2020, 17(3): 1873–1881Google ScholarCross Ref
- Ravi C. Fuzzy Crow Search Algorithm-Based Deep LSTM for Bitcoin Prediction [J]. International Journal of Distributed Systems and Technologies (IJDST), 2020, 11(4):53–71Google ScholarDigital Library
- Hochreiter S , Schmidhuber J. Long Short-Term Memory [J]. Neural Computation, 1997, 9(8):1735–1780Google ScholarDigital Library
- Williams R, Zipser D. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks [J]. Neural Computation, 2014, 1(2):270–280Google ScholarDigital Library
- Li Y, Zhu Z, Kong D, EA-LSTM: Evolutionary attention-based LSTM for time series prediction [J]. Knowledge-Based Systems, 2019, 181: 104785Google ScholarDigital Library
- Namin A H, Leboeuf K, Muscedere R, Efficient hardware implementation of the hyperbolic tangent sigmoid function [C]. 2009 IEEE International Symposium on Circuits and Systems, IEEE, 2009, 16: 2117–2120Google Scholar
- Ouyang J, Lin S, Qi W, SDA: Software-defined accelerator for large-scale DNN systems [C]. 2014 IEEE Hot Chips 26 Symposium (HCS), IEEE, 2014, 26: 1–23.Google Scholar
- Yuxing Z , Min D . A Traffic Flow Prediction Method Based on Long-term Deep Convolution Network [J]. Geomatics & Spatial Information Technology, 2019, 121: 304–312Google Scholar
- Fernandez R , Rendel A , Ramabhadran B , Prosody Contour Prediction with Long Short-Term Memory, Bi-Directional, Deep Recurrent Neural Networks [C]. Interspeech 2014, 2014, 2268–2272Google Scholar
- Cao P, Yang Z, Sun L, Short-term power load forecasting using integrated methods based on long short-term memory [J]. Neural Processing Letters, 2019, 50(1):103–119Google ScholarDigital Library
- Wenhao F, Yuanan L, Fan W U, Optimal resource allocation for transmission diver-sity in multi-radio access networks:a coevolutionary genetic algorithm approach [J]. Science China(Information Sciences), 2014, 2: 22310Google Scholar
- Goldberg D E. Genetic Algorithm in Search Optimization and Machine Learning [J]. Addison Wesley, 1989, xiii(7): 2104–2116Google ScholarDigital Library
- Ren M, Zeng W, Yang B, Learning to reweight examples for robust deep learning [C]. International Conference on Machine Learning. PMLR, 2018: 4334–4343.Google Scholar
- Salah B, Ali F, Ali O, Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches [J]. Energies, 2018, 11(7):1636–1656Google ScholarCross Ref
- Brinkerink M, Deane P, Collins S, Developing a global interconnected power system model [J]. Global Energy Interconnection, 2018, 1(3): 330–343Google Scholar
- Pathak J, Hunt B, Girvan M, Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach [J]. Physical Review Letters, 2018, 120(2): 24102.1–24102.5Google ScholarCross Ref
- Griffith A, Pomerance A, Gauthier D J. Forecasting chaotic systems with very low connectivity reservoir computers [J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019, 29(12): 123108–123113Google ScholarCross Ref
- Hashemi R R, Ardakani O M, Bahrami A A, A Mediated Multi-RNN Hybrid System for Prediction of Stock Prices [C]. 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2020, 11: 382–387Google Scholar
- Li C, Tang G, Xue X, Short-term Wind Speed Interval Prediction based on Ensemble GRU model [J]. IEEE Transactions on Sustainable Energy, 2019, 20(99):1–1Google Scholar
- Lance C E , Dawson B , Birkelbach D , Method Effects, Measurement Error, and Substantive Conclusions [J]. Organizational Research Methods, 2010, 13(3):435–455Google ScholarCross Ref
- Li Z, Li L. A Hybrid Model of Least Squares Support Vector Regression Optimized by Particle Swarm Optimization for Electricity Demand Prediction [C]. Proceedings of the 2019 11th International Conference on Machine Learning and Computing. 2019,11: 91–103Google ScholarDigital Library
- Mujeeb S , Javaid N , Ilahi M , Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities [J]. Sustainability, 2019, 11(4): 987–1016Google ScholarCross Ref
- Qureshi A S , Khan A , Zameer A , Wind power prediction using deep neural network based meta regression and transfer learning [J]. Applied Soft Computing, 2017, 58:742–755Google ScholarCross Ref
- Wang J, Ersoy O K, He M, Multi-offspring genetic algorithm and its application to the traveling salesman problem [J]. Applied Soft Computing, 2016, 43: 415–423Google ScholarDigital Library
- Tran V T, Yang B S, Oh M S, Machine condition prognosis based on regression trees and one-step-ahead prediction [J]. Mechanical Systems & Signal Processing, 2008, 22(5):1179–1193Google ScholarCross Ref
- Raikova R T, Prilutsky B I. Sensitivity of predicted muscle forces to parameters of the optimization-based human leg model revealed by analytical and numerical analyses [J]. Journal of Biomechanics, 2001, 34(10):1243–1255Google ScholarCross Ref
- Zhang Y, Zheng L. Pedestrian Trajectory Prediction with MLP-social-GRU [C]. 2021 13th International Conference on Machine Learning and Computing. 2021, 13: 368–372.Google ScholarDigital Library
- Maya S, Ueno K, Nishikawa T. dLSTM: a new approach for anomaly detection using deep learning with delayed prediction [J]. International Journal of Data Science and Analytics, 2019, 8(2): 137–164Google ScholarCross Ref
- Verstraeten D, Schrauwen B, M. D Haene, An experimental unification of reservoir computing methods. Neural Networks, 2007, 20(3):391–403Google ScholarDigital Library
- Badjate S L, Dudul S V. Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series [J]. Applied Computational Intelligence & Soft Computing, 2009, 2009(1):2–11Google Scholar
Recommendations
Chronological penguin Adam-based deep long short-term memory classifier for stock market prediction
This paper proposes an effective method named chronological penguin Adam-based deep long short-term memory (CPAdam-based deep LSTM) to forecast the future prices in the stock market. Initially, the technical indicators are extracted from the stock market ...
A deep learning approach for stock market prediction using deep autoencoder and long short-term memory
The stock market prediction problems have received increased attention from researchers due to the high stakes involved and the need for better prediction accuracy. We have developed an architecture by combining a deep autoencoder and long short-term ...
Financial Time Series Prediction Based on Deep Learning
By combining wavelet analysis with Long Short-Term Memory (LSTM) neural network, this paper proposes a time series prediction model to capture the complex features such as non-linearity, non-stationary and sequence correlation of financial time series. ...
Comments