135
Views
0
CrossRef citations to date
0
Altmetric
Research Article

An Integration of Genetic Feature Selector, Histogram-Based Outlier Score, and Deep Learning for Wind Turbine Power Prediction

, , ORCID Icon &
Pages 9342-9365 | Received 22 Apr 2022, Accepted 20 Sep 2022, Published online: 11 Oct 2022

References

  • Alam, T. 2020. Genetic algorithm: Reviews, implementations, and applications. International Journal of Engineering Pedagogy. 10(6):57. doi:10.3991/ijep.v10i6.14567.
  • Chen, X. 2021. Deep learning-based prediction of wind power for multi-turbines in a wind farm. Frontiers in Energy Research 9:403. doi:10.3389/fenrg.2021.723775.
  • Chicco, D. 2021. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science 7:e623.
  • Dehnavi, S. D. 2020. New deep learning-based approach for wind turbine output power modeling and forecastingIEEE Transactions on Industry Applications. New deep learning-based approach for wind turbine output power modeling and forecasting. doi:10.1109/TIA.2020.3002186.
  • Erdinc, O. 2017. Optimization in renewable energy systems: Recent perspectives. Oxford, United Kingdom: Butterworth- Heinemann.
  • Fahim, P. A method based on fast Fourier transform for online supervising of power system and control structure design. In 7th Iran Wind Energy Conference, shahrood, Iran. IEEE, 2021.
  • Fahim, P. 2022. Data-driven techniques for optimizing the renewable energy systems operations. In Handbook of smart energy systems, ed. I. I. A. I. Fathi, I. I. A. I. Zio, and I. I. A. I. C. U. M. Pardalos, et al. Cham: Springer.
  • Fan, J. 2019. Light gradient boosting machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agricultural Water Management 225:105758.
  • Hosseini-Sani, K. 2017. Dmc versus gain scheduled pi controller for pitch regulation of 100 kw wind turbine. The Modares Journal of Electrical Engineering 16(4):1–9.
  • Jadhav, S. 2018. Information gain directed genetic algorithm wrapper feature selection for credit rating. Applied Soft Computing 69:541–53.
  • Kamrani, A. K. 2020 . Group technology and cellular manufacturing: Methodologies and applications. United Kingdom: Taylor & Francis.
  • Ke, G. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30:3146–54.
  • Kisvari, A. 2021. Wind power forecasting–a data-driven method along with gated recurrent neural network. Renewable Energy 163:1895–909.
  • Kuchaki Rafsanjani, M., A. Rezaei, A. Shahraki, and A. Borumand Saeid. 2014. Qarima: A new approach to prediction in queue theory. Applied Mathematics and Computation 244: 514–25.doi: 10.1016/j.amc.2014.06.108.
  • Larriva-Novo, X. , 2021. An iot-focused intrusion detection system approach based on preprocessing characterization for cybersecurity datasets. Sensors 21(2):656.
  • Li, Y. 2020. Comparative study of onshore and offshore wind characteristics and wind energy potentials: A case study for southeast coastal region of china. Sustainable Energy Technologies and Assessments 39:100711.
  • Lin, Z. 2020. Wind power forecasting of an offshore wind turbine based on high-frequency scada data and deep learning neural network. Energy 201:117693.
  • Lin, Z. 2020. Wind power prediction based on high-frequency scada data along with isolation forest and deep learning neural networks. International Journal of Electrical Power & Energy Systems 118:105835.
  • Lin, J. 2022. Short-term load forecasting based on lstm networks considering attention mechanism. International Journal of Electrical Power & Energy Systems 137:107818.
  • Liu, X. 2021. Short-term predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning. Energy 217:119356.
  • Lohrasbinasab, I. 2021. From statistical-to machine learning-based network traffic prediction. Transactions on Emerging Telecommunications Technologies. 33(4):e4394. doi:10.1002/ett.4394.
  • Mambwe Kasongo, S. 2021. Genetic algorithm based feature selection technique for optimal intrusion detection Preprints. doi:10.20944/preprints202106.0710.v1.
  • Mortazavi, R. 2021. Wrapper-based feature selection using regression trees to predict intrinsic viscosity of polymer. Engineering with Computers. 38(3):1–13. doi:10.1007/s00366-020-01226-1.
  • Muhammad Shahani, N. 2022. Predictive modeling of drilling rate index using machine learning approaches: Lstm, simple rnn, and rfa. Petroleum Science and Technology 40(5):534–55.
  • Nanda, S. 2018. Analysis of the performance of classifiers on wavelet features with pca and ga for the detection of breast cancer in ultrasound images. IOSR Journal of VLSI and Signal Processing 8(1):16–24.
  • Neshat, M. 2021. Wind turbine power output prediction using a new hybrid neuro- evolutionary method. Energy 229:120617.
  • Nielson, J. 2020. Using atmospheric inputs for artificial neural networks to improve wind turbine power prediction. Energy 190:116273.
  • Samet, H. 2021. Prediction of wind farm reactive power fast variations by adaptive one-dimensional convolutional neural network. Computers & Electrical Engineering 96:107480.
  • Sayed, S. 2019. A nested genetic algorithm for feature selection in high-dimensional cancer microarray datasets. Expert Systems with Applications 121:233–43.
  • Shahraki, A. 2019. An outlier detection method to improve gathered datasets for network behavior analysis in iot. Journal of Communications 14:455–62.
  • Smiti, A. 2020. A critical overview of outlier detection methods. Computer Science Review 38:100306. doi: 10.1016/j.cosrev.2020.100306
  • Sun, L. 2021. Real-time power prediction approach for turbine using deep learning techniques. Energy 233:121130. doi:10.1016/j.energy.2021.121130.
  • Tony Liu, F. et al. Isolation forest. In 2008 eighth ieee international conference on data mining 2008 Pisa, Italy, pages 413–22. IEEE, 2008.
  • Vaezi, N. et al. 2019, cite this year Elimination of pitch signal fluctuations caused by unbalanced aerodynamic system in 100 kw wind turbine. In 2019 27th Iranian Conference on Electrical Engineering (ICEE), pages 446–50. IEEE.
  • Vaezi, N. 2015. Design mv-str pitch controller for 100 kw wind turbine International Congress on Technology, Communication and Knowledge (ICTCK) Mashhad, Iran.
  • Woo, S. et al. Predicting wind turbine power and load outputs by multi-task convolutional lstm model. In Power & Energy Society General Meeting Portland, OR, USA. IEEE, 2018.
  • Zhang, S. 2018. A novel knn algorithm with data-driven k parameter computation. Pattern Recognition Letters 109:44–54.
  • Zhang, M. 2022. Health factor extraction of lithium- ion batteries based on discrete wavelet transform and soh prediction based on catboost. Energies 15(15):5331. doi:10.3390/en15155331.
  • Zhang, Y. 2022. The prediction of spark-ignition engine performance and emissions based on the svr algorithm. Processes 10(2):312.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.