Abstract
Accurate short-term load forecasting is crucial for the steady operation of the power system and power market schedule planning. The extraction of features and training of prediction models are challenging as the load series is extremely volatile and nonlinear. To address the above issues, we propose a deep bidirectional long short-term memory (DBiLSTM) network based on variational mode decomposition (VMD) and an attention mechanism, in which the model hyperparameters are optimized using the improved particle swarm optimization (IPSO) technique. In this study, the mode number k of the VMD is determined by the ratio of residual energy following decomposition. Subsequently, the DBiLSTM is stacked using multiple layers of BiLSTM for a more precise representation of time-series data and the capturing of information at different scales, thereby enabling nonlinear load sequence forecasting and enhancing the accuracy. Finally, the IPSO uses nonlinear decreasing inertia weights to overcome the drawbacks of premature convergence and local optima. The effectiveness and progress of the proposed method are evaluated using the power load dataset from the ninth electrical attribute modeling competition test questions.
Similar content being viewed by others
References
Fallah SN, Deo RC, Shojafar M, Conti M, Shamshirband S (2018) Computational intelligence approaches for energy load forecasting in smart energy management grids: State of the art, future challenges, and research directions. Energies 11(3):596. https://doi.org/10.3390/en11030596
Raza MQ, Khosravi A (2015) A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sust Energ Rev 50:1352–1372. https://doi.org/10.1016/j.rser.2015.04.065
Huang S-J, Shih K-R (2003) Short-term load forecasting via arma model identification including non-gaussian process considerations. IEEE Trans Power Syst 18(2):673–679. https://doi.org/10.1109/TPWRS.2003.811010https://doi.org/10.1109/TPWRS.2003.811010
M. A-HH, A. SS (2006) Fuzzy short-term electric load forecasting using kalman filter. Iee Proc Gener Transm Distrib 153(2):217–227. https://doi.org/10.1049/ip-gtd:20050088
Ceperic E, Ceperic V, Baric A (2013) A strategy for short-term load forecasting by support vector regression machines. IEEE Trans Power Syst 28(4):4356–4364. https://doi.org/10.1109/TPWRS.2013.2269803https://doi.org/10.1109/TPWRS.2013.2269803
Huang N, Lu G, Xu D (2016) A permutation importance-based feature selection method for short-term electricity load forecasting using random forest. Energies 9(10):767. https://doi.org/10.3390/en9100767
Zheng Z, Chen H, Luo X (2018) Subsampled support vector regression ensemble for short term electric load forecasting. Energy 164(10):160–170. https://doi.org/10.1016/j.energy.2018.08.169https://doi.org/10.1016/j.energy.2018.08.169
Zheng Z, Chen H, Luo X (2019) A kalman filter-based bottom-up approach for household short-term load forecast. Appl Energ 250(10):882–894. https://doi.org/10.1016/j.apenergy.2019.05.102
Wang K, Qi X, Liu H (2019) A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Appl Energ 251:113315. https://doi.org/10.1016/j.apenergy.2019.113315https://doi.org/10.1016/j.apenergy.2019.113315
Sehovac L, Grolinger K (2020) Deep learning for load forecasting: Sequence to sequence recurrent neural networks with attention. IEEE Access 8:36411–36426. https://doi.org/10.1109/ACCESS.2020.2975738https://doi.org/10.1109/ACCESS.2020.2975738
Ungureanu S, Topa V, Cziker AC (2021) Deep learning for short-term load forecasting-industrial consumer case study. Appl Sci-Basel 11(21):10126. https://doi.org/10.3390/app112110126
Aslam S, Herodotou H, Mohsin SM, Javaid N, Ashraf N, Aslam S (2021) A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renew Sust Energ Rev 144:110992. https://doi.org/10.1016/j.rser.2021.110992https://doi.org/10.1016/j.rser.2021.110992
Gasparin A, Lukovic S, Alippi C (2022) Deep learning for time series forecasting: The electric load case. CAAI Trans Intell Technol 7(1):1–25. https://doi.org/10.1049/cit2.12060
Huang K, Wu S, Li F, Yang C, Gui W (2021) Fault diagnosis of hydraulic systems based on deep learning model with multirate data samples. IEEE Transactions on neural networks and learning systems, pp 1–13. https://doi.org/10.1109/TNNLS.2021.3083401https://doi.org/10.1109/TNNLS.2021.3083401
Chen M-R, Zeng G-Q, Lu K-D, Weng J (2019) A two-layer nonlinear combination method for short-term wind speed prediction based on elm, enn, and lstm. IEEE Inter Things J 6(4):6997–7010. https://doi.org/10.1109/JIOT.2019.2913176
Zhao F, Zeng G-Q, Lu K-D (2019) Enlstm-wpeo: Short-term traffic flow prediction by ensemble lstm, nnct weight integration, and population extremal optimization. IEEE Trans Veh Technol 69(1):101–113. https://doi.org/10.1109/TVT.2019.2952605
Zhang B, Wu J-L, Chang P-C (2018) A multiple time series-based recurrent neural network for short-term load forecasting. 12 22:4099–4112. https://doi.org/10.1007/s00500-017-2624-5
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735https://doi.org/10.1162/neco.1997.9.8.1735
Tian C, Ma J, Zhang C, Zhan P (2018) A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies 11(12). https://doi.org/10.3390/en11123493
Jiao R, Zhang T, Jiang Y, He H (2018) Short-term non-residential load forecasting based on multiple sequences lstm recurrent neural network. IEEE Access 6:59438–59448. https://doi.org/10.1109/ACCESS.2018.2873712https://doi.org/10.1109/ACCESS.2018.2873712
Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2019) Short-term residential load forecasting based on lstm recurrent neural network. IEEE Trans Smart Grid 10(1):841–851. https://doi.org/10.1109/TSG.2017.2753802
Petrosanu D-M, Pirjan A (2021) Electricity consumption forecasting based on a bidirectional long-short-term memory artificial neural network. Sustainability 13(1):104. https://doi.org/10.3390/su13010104https://doi.org/10.3390/su13010104
Kiruthiga D, Manikandan V (2021) Intraday time series load forecasting using bayesian deep learning method-a new approach. ELECTRICAL ENGINEERING. https://doi.org/10.1007/s00202-021-01411-5https://doi.org/10.1007/s00202-021-01411-5
Zheng J, Zhang L, Chen J, Wu G, Ni S, Hu Z, Weng C, Chen Z (2021) Multiple-load forecasting for integrated energy system based on copula-dbilstm. Energies 14(8):2188. https://doi.org/10.3390/en14082188https://doi.org/10.3390/en14082188
Cai C, Tao Y, Zhu T, Deng Z (2021) Short-term load forecasting based on deep learning bidirectional lstm neural network. Appl Sci-Basel 11(17):8129. https://doi.org/10.3390/app11178129
Zheng H, Yuan J, Chen L (2017) Short-term load forecasting using emd-lstm neural networks with a xgboost algorithm for feature importance evaluation. Energies 10(8):1168. https://doi.org/10.3390/en10081168
Qiu X, Ren Y, Suganthan PN, Amaratunga G (2017) Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl Soft Comput 54:246–255. https://doi.org/10.1016/j.asoc.2017.01.015
Bedi J, Toshniwal D (2018) Empirical mode decomposition based deep learning for electricity demand forecasting. IEEE Access 6:49144–49156. https://doi.org/10.1109/ACCESS.2018.2867681
Meng Z, Xie Y, Sun J (2021) Short-term load forecasting using neural attention model based on emd. ELECTRICAL ENGINEERING. https://doi.org/10.1007/s00202-021-01420-4
Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544. https://doi.org/10.1109/TSP.2013.2288675
Li W, Quan C, Wang X, Zhang S (2018) Short-term power load forecasting based on a combination of vmd and elm. Pol J Environ Stud 27(5):2143–2154. https://doi.org/10.15244/pjoes/78244
Zhou M, Hu T, Bian K, Lai W, Hu F, Hamrani O, Zhu Z (2021) Short-term electric load forecasting based on variational mode decomposition and grey wolf optimization. Energies 14(16):4890. https://doi.org/10.3390/en14164890
Wang S, Wang X, Wang S, Wang D (2019) Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting. Int J Electr Power Energy Syst 109:470–479. https://doi.org/10.1016/j.ijepes.2019.02.022https://doi.org/10.1016/j.ijepes.2019.02.022
Qin J, Zhang Y, Fan S, Hu X, Huang Y, Lu Z, Liu Y (2022) Multi-task short-term reactive and active load forecasting method based on attention-lstm model. Int J Electr Power Energy Syst 135:107517. https://doi.org/10.1016/j.ijepes.2021.107517
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. Computer Science. https://doi.org/10.48550/arXiv.1409.0473
Lu K-D, Wu Z-G (2022) Constrained-differential-evolution-based stealthy sparse cyber-attack and countermeasure in an ac smart grid. IEEE Trans Industr Inform 18(8):5275–5285. https://doi.org/10.1109/TII.2021.3129487
Lu K, Zhou W, Zeng G, Zheng Y (2019) Constrained population extremal optimization-based robust load frequency control of multi-area interconnected power system. Int J Electr Power Energy Syst 105:249–271. https://doi.org/10.1016/j.ijepes.2018.08.043https://doi.org/10.1016/j.ijepes.2018.08.043
Wang L, Liu Y, Li T, Xie X, Chang C (2020) Short-term pv power prediction based on optimized vmd and lstm. IEEE Access 8:165849–165862. https://doi.org/10.1109/ACCESS.2020.3022246https://doi.org/10.1109/ACCESS.2020.3022246
Pei Y, Zhenglin L, Qinghui Z, Yixiao W, Yanli L, Chaolong H (2021) Load forecasting of refrigerated display cabinet based on ceemd-ipso-lstm combined model. Open Physics 19(1):360–374. https://doi.org/10.1515/phys-2021-0043
Liu Y, Yang C, Huang K, Gui W (2020) Non-ferrous metals price forecasting based on variational mode decomposition and lstm network. Knowl-Based Syst 188:105006. https://doi.org/10.1016/j.knosys.2019.105006https://doi.org/10.1016/j.knosys.2019.105006
Yan L, Zhang H (2021) A variant model based on bilstm for electricity load prediction, pp 404–411. https://doi.org/10.1109/ICPICS52425.2021.9524223https://doi.org/10.1109/ICPICS52425.2021.9524223
Du J, Cheng Y, Zhou Q, Zhang J, Zhang X, Li G (2020) Power load forecasting using bilstm-attention, vol 440, p 032115. https://doi.org/10.1088/1755-1315/440/3/032115
Song J, Xue G, Pan X, Ma Y, Li H (2020) Hourly heat load prediction model based on temporal convolutional neural network. IEEE Access 8:16726–16741. https://doi.org/10.1109/ACCESS.2020.2968536https://doi.org/10.1109/ACCESS.2020.2968536
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Junhao Yu, Xiaohong Dai and Yuanyuan Li are contributed equally to this work.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Huang, Y., Huang, Z., Yu, J. et al. Short-term load forecasting based on IPSO-DBiLSTM network with variational mode decomposition and attention mechanism. Appl Intell 53, 12701–12718 (2023). https://doi.org/10.1007/s10489-022-04174-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-022-04174-z