Abstract
In this manuscript, wind speed multistep forecasting model using a hybrid decomposition technique and a selfish herd optimizer-based deep neural network is proposed. The reliability and hygiene standards of wind energy are obtaining large stake. Indeed, it is difficult to ascertain a scientific and robust forecasting method due to the variability and the wind speed intervention. To wind farm operational scheduling, accurate and consistent prediction is crucial. Therefore, the wind speed array usually has dynamic characteristics including nonlinearity and variability, rendering the estimation of wind energy exceptionally challenging. The proposed hybrid decomposition technique incorporates the multivariate empirical mode decomposition (MEMD) with the specific enhanced empirical wavelet transform and is primarily utilized to progressively decompose MEMD’s high-intrinsic mode functions (IMFs). Then, strengthened DNN is widely used for the forecasting of all decomposed IMFs, so the components are using selfish herd optimizer algorithm. The data from Tamil Nadu region for certain coastal and hilly areas are used for multiforecasting to ascertain the predicting potential of the proposed method. The experimental outcomes demonstrate that the hypothesized model executes substantially better in the one five-step wind speed predicting than all other perceived models, suggesting that the proposed prototype is well suited to standardized multistep wind speed prediction.
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References
Agarwal A, Sengar A, Debnath S (2017) A novel noise floor estimation technique for optimized thresholding in spectrum sensing. In: 2017 international conference on advances in computing, communications and informatics (ICACCI).607_611. https://doi.org/10.1109/icacci.2017.8125907
Alatas B (2019) Sports inspired computational intelligence algorithms for global optimization. ArtifIntell Rev 52(3):1579–1627. https://doi.org/10.1007/s10462-017-9587-x
Begam K, Deepa S (2019) Optimized nonlinear neural network architectural models for multistep wind speed forecasting. ComputElectrEng 78:32–49. https://doi.org/10.1016/j.compeleceng.2019.06.018
Besnassi M, Neggaz N, Benyettou A (2020) Face detection based on evolutionary Haar filter. Pattern Anal Appl 23(1):309–330. https://doi.org/10.1007/s10044-019-00784-5
Bharani R, Sivaprakasam A (2019) A large volume wind data for renewable energy applications. Data Br 25:104291. https://doi.org/10.1016/j.dib.2019.104291
Bui QT, Nguyen QH, Nguyen XL, Pham VD, Nguyen HD, Pham VM (2020) Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. J Hydrol 581:124379. https://doi.org/10.1016/j.jhydrol.2019.124379
Fausto F, Cuevas E, Valdivia A, González A (2017) A global optimization algorithm inspired in the behavior of selfish herds. Biosystems 160:39–55. https://doi.org/10.1016/j.biosystems.2017.07.010
Fu W, Wang K, Li C, Tan J (2019) Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM. Energy Convers Manag 187:356–377. https://doi.org/10.1016/j.enconman.2019.02.086
Gadekallu TR, Khare N, Bhattacharya S, Singh S, Reddy Maddikunta PK, Ra IH, Alazab M (2020) Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics 9(2):274. https://doi.org/10.3390/electronics9020274
He Q, Wang J, Lu H (2018) A hybrid system for short-term wind speed forecasting. Appl Energy 226:756–771. https://doi.org/10.1016/j.apenergy.2018.06.053
Huang G, Su Y, Kareem A, Liao H (2016) Time-frequency analysis of nonstationary process based on multivariate empirical mode decomposition. J EngMech 142(1):04015065. https://doi.org/10.1061/(asce)em.1943-7889.0000975
Jiang Y, Huang G (2017) Short-term wind speed prediction: hybrid of ensemble empirical mode decomposition, feature selection and error correction. Energy Convers Manag 144:340–350. https://doi.org/10.1016/j.enconman.2017.04.064
Jiang P, Yang H, Heng J (2019) A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting. Appl Energy 235:786–801. https://doi.org/10.1016/j.apenergy.2018.11.012
Knödtel J, Fritscher M, Reiser D, Fey D, Breiling M, Reichenbach M (2020) A Model-to-circuit compiler for evaluation of DNN accelerators based on systolic arrays and multibit emerging memories. In: 2020 9th international conference on modern circuits and systems technologies (MOCAST). IEEE, pp 1–6. https://doi.org/10.1109/MOCAST49295.2020.9200241
Li C, Zhu Z, Yang H, Li R (2019) An innovative hybrid system for wind speed forecasting based on fuzzy preprocessing scheme and multi-objective optimization. Energy 174:1219–1237. https://doi.org/10.1016/j.energy.2019.02.194
Liu H, Chen C, Tian H, Li Y (2012) A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renew Energy 48:545–556. https://doi.org/10.1016/j.renene.2012.06.012
Liu H, Duan Z, Han F, Li Y (2018a) Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm. Energy Convers Manag 156:525–541. https://doi.org/10.1016/j.enconman.2017.11.049
Liu H, Duan Z, Li Y, Lu H (2018b) A novel ensemble model of different mother wavelets for wind speed multi-step forecasting. Appl Energy 228:1783–1800. https://doi.org/10.1016/j.apenergy.2018.07.050
Liu H, Mi X, Li Y (2018c) Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Convers Manag 159:54–64. https://doi.org/10.1016/j.enconman.2018.01.010
Liu H, Mi X, Li Y (2018d) Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Convers Manag 156:498–514. https://doi.org/10.1016/j.enconman.2017.11.053
Maddikunta PKR, Gadekallu TR, Kaluri R, Srivastava G, Parizi RM, Khan MS (2020) Green communication in IoT networks using a hybrid optimization algorithm. ComputCommun. https://doi.org/10.1016/j.comcom.2020.05.020
Mirjalili S, Dong JS, Sadiq AS, Faris H (2020) Genetic algorithm: theory, literature review, and application in image reconstruction. In: Mirjalili S, Song DJ, Lewis A (eds) Nature-inspired optimizers. Springer, Cham, pp 69–85. https://doi.org/10.1007/978-3-030-12127-3_5
Mythili S, Thiyagarajah K, Rajesh P, Shajin FH (2020) Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: an antlionoptimiser and invasive weed optimisation algorithm. HKIE Trans 27(1):25–37. https://doi.org/10.33430/V27N1THIE-2018-0024
Naik J, Satapathy P, Dash P (2018) Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression. Appl Soft Comput 70:1167–1188. https://doi.org/10.1016/j.asoc.2017.12.010
Neggaz N, Houssein EH, Hussain K (2020) An efficient henry gas solubility optimization for feature selection. Expert SystAppl 152:113364. https://doi.org/10.1016/j.eswa.2020.113364
Qiu X, Ren Y, Suganthan P, 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
Qu Z, Mao W, Zhang K, Zhang W, Li Z (2019) Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network. Renew Energy 133:919–929. https://doi.org/10.1016/j.renene.2018.10.043
Rajesh P, Shajin FH (2020) A multi-objective hybrid algorithm for planning electrical distribution system. IntInfEngTechnolAssoc. https://doi.org/10.18280/ejee.224-509
Reddy GT, Khare N (2017) Hybrid firefly-bat optimized fuzzy artificial neural network based classifier for diabetes diagnosis. Int J IntellEngSyst 10(4):18–27
Safari N, Chung C, Price G (2018) Novel multi-step short-term wind power prediction framework based on chaotic time series analysis and singular spectrum analysis. IEEE Trans Power Syst 33(1):590–601. https://doi.org/10.1109/tpwrs.2017.2694705
Shajin FH, Rajesh P (2020) Trusted Secure Geographic Routing Protocol: outsider attack detection in mobile ad hoc networks by adopting trusted secure geographic routing protocol. Int J Pervasive ComputCommun. https://doi.org/10.1108/IJPCC-09-2020-0136
Sharma S, Ghosh S (2016) Short-term wind speed forecasting: application of linear and non-linear time series models. Int J Green Energy 13(14):1490–1500. https://doi.org/10.1080/15435075.2016.1212200
Thota MK, Shajin FH, Rajesh P (2020) Survey on software defect prediction techniques. Int J ApplSciEng 17(4):331–344. https://doi.org/10.6703/IJASE.202012_17(4).331
Transpire Online (2019) Flower Pollination Algorithm (FPA): a novel method motivated from the behavior of flowers for optimal solution (2020) Transpire Online. Retrieved 6 May 2020, from https://transpireonline.blog/2020/01/27/flower-pollination-algorithm-fpa-a-novel-method-motivated-from-the-behavior-of-flowers-for-optimal-solution
Vahidi B, ForoughiNematolahi A (2019) Physical and physic-chemical based optimization methods: a review. J Soft ComputCivEng 3(4):12–27. https://doi.org/10.22115/SCCE.2020.214959.1161
Wang C, Zhang H, Fan W, Ma P (2017a) A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction. Energy 138:977–990. https://doi.org/10.1016/j.energy.2017.07.112
Wang D, Luo H, Grunder O, Lin Y (2017b) Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction. Renew Energy 113:1345–1358. https://doi.org/10.1016/j.renene.2017.06.095
Xiao L, Qian F, Shao W (2017) Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm. Energy Convers Manag 143:410–430. https://doi.org/10.1016/j.enconman.2017.04.012
Xue B, Hong H, Zhou S, Chen G, Li Y, Wang Z, Zhu X (2019) Morphological filtering enhanced empirical wavelet transform for mode decomposition. IEEE Access 7:14283–14293. https://doi.org/10.1109/access.2019.2892764
Yao Z, Wang C (2018) A hybrid model based on a modified optimization algorithm and an artificial intelligence algorithm for short-term wind speed multi-step ahead forecasting. Sustainability 10(5):1443. https://doi.org/10.3390/su10051443
Zhang Y, Liu K, Qin L, An X (2016) Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods. Energy Convers Manag 112:208–219. https://doi.org/10.1016/j.enconman.2016.01.023
Zhang W, Qu Z, Zhang K, Mao W, Ma Y, Fan X (2017) A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting. Energy Convers Manag 136:439–451. https://doi.org/10.1016/j.enconman.2017.01.022
Zhou X, Zhang M, Xu Z, Cai C, Huang Y, Zheng Y (2019) Shallow and deep neural network training by water wave optimization. Swarm EvolutComput 50:100561. https://doi.org/10.1016/j.swevo.2019.100561
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Vidya, S., Srie Vidhya Janani, E. Wind speed multistep forecasting model using a hybrid decomposition technique and a selfish herd optimizer-based deep neural network. Soft Comput 25, 6237–6270 (2021). https://doi.org/10.1007/s00500-021-05608-5
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DOI: https://doi.org/10.1007/s00500-021-05608-5