THERMAL SCIENCE

International Scientific Journal

Fatimah Almulhim

,

Ben Anis Ishak

,

Azhari A. Elhag

,

Hassan M. Aljohani

,

Sayed Abdel-Khalek

SHORT TERM WIND SPEED PREDICTION USING SEARCH AND RESCUE OPTIMIZATION WITH DEEP BELIEF NETWORK ON SCATTEROMETRY DATA

ABSTRACT
Scatterometry is a technique used to transmit radio or microwaves to examine different geophysical properties, wind speed, and direction. Precise and rapid weather predictions become essential in several applications in assisting planning and management in response to weather conditions. At the same time, timely wind speed prediction gains considerable attention in several economical, business, and management areas. With the consideration of wind speed as an arbitrary variable, precise wind speed prediction using machine learning and deep learning models can be established. With this motivation, this study develops a short-term wind speed prediction using search and rescue optimization with deep belief network (STWSP-SRDBN) model. To accomplish accurate wind speed prediction, the STWSP-SRDBN method initially pre-processes the weather data using min-max normalization. Additionally, the STWSP-SRDBN model utilizes DBN model to predict the weather data. Moreover, the SRO algorithm is utilized to fine tune the hyperparameters related to the DBN approach. The presented STWSP-SRDBN method makes use of Spatio-temporal multivariate multi-dimensional historical weather data to learn new representations utilized for wind forecasting. The experimental validation of the STWSP-SRDBN method is tested using a set of weather data and the outcomes are investigated under numerous aspects. The experimental results indicated the enhanced outcomes of the STWSP-SRDBN method over recent state of art methods.
KEYWORDS
wind speed prediction, search optimization, prediction models, time-series forecasting, hyperparameter tuning, deep learning
PAPER SUBMITTED: 2024-07-15
PAPER REVISED: 2024-10-03
PAPER ACCEPTED: 2024-10-29
PUBLISHED ONLINE: 2025-01-25
DOI REFERENCE: https://doi.org/10.2298/TSCI2406097A
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 6, PAGES [5097 - 5111]
REFERENCES
  1. Nezhad, M. M., et al., Wind Source Potential Assessment Using Sentinel 1 Satellite and a New Forecasting Model Based on Machine Learning: A Case Study Sardinia Islands, Renewable Energy, 155 (2020), Aug., pp. 212-224
  2. Liu, W. T., Progress in Scatterometer Application, Journal of Oceanography, 58 (2002), Feb., pp. 121-136
  3. Ribal, A., Young, I. R., Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations, Journal of Atmospheric and Oceanic Technology, 37 (2020), 2, pp. 279-297
  4. Wang, Z., et al., Inconsistencies in Scatterometer wind Products Based on ASCAT and OSCAT-2 Collocations, Remote Sensing of Environment, 225 (2019), May, pp. 207-216
  5. Kumar, S. V. A., et al., Comparative Study of Offshore Winds and Wind Energy Production Derived from Multiple Scatterometers and Met Buoys, Energy, 185 (2019), Oct., pp. 599-611
  6. Wang, J., et al., Analysis and Application of Forecasting Models in Wind Power Integration: A Review of Multi-Step-Ahead Wind Speed Forecasting Models, Renewable and Sustainable Energy Reviews, 60 (2016), July, pp. 960-981
  7. Senthil, K. P., Improved Prediction of Wind Speed Using Machine Learning, EAI Endorsed Transactions on Energy Web, 6 (2019), 157033
  8. Khosravi, A., et al., Time-Series Prediction of Wind Speed Using Machine Learning Algorithms: A Case Study Osorio Wind Farm, Brazil. Applied Energy, 224 (2018), Aug., pp. 550-566
  9. Luo, X., Short-Term Wind Speed Forecasting Via Stacked Extreme Learning Machine with Generalized Correntropy, IEEE Transactions on Industrial Informatics, 14 (2018), 11, pp. 4963-4971
  10. Yu, C., Li, A Novel Framework for Wind Speed Prediction Based on Recurrent Neural Networks and Support Vector Machine, Energy Conversion and Management, 178 (2018), Dec., pp. 137-145
  11. Valsaraj, P., et al., Spatio-Temporal Independent Applicability of one Time Trained Machine Learning Wind Forecast Models: A Promising Case Study from the Wind Energy Perspective, International Journal of Sustainable Energy, 41 (2022), 9, pp. 1164-1182
  12. Hur, S. H., Short-Term Wind Speed Prediction Using Extended Kalman Filter and Machine Learning, Energy Reports, 7 (2021), Nov., pp. 1046-1054
  13. Demolli, H., et al., Wind Power Forecasting Based on Daily Wind Speed Data Using Machine Learning Algorithms, Energy Conversion and Management, 198 (2019), 111823
  14. Li, R., and Jin, Y., A Wind Speed Interval Prediction System Based on Multi-Objective Optimization for Machine Learning Method, Applied Energy, 228 (2018), Oct., pp. 2207-2220
  15. Zhu, Q., et al., Wind Speed Prediction With Spatio-Temporal Correlation: A Deep Learning Approach, Energies, 11 (2018), 705
  16. Li, L., Geographically Weighted Machine Learning and Downscaling for High-Resolution Spatiotemporal Estimations of Wind Speed, Remote Sensing, 11 (2019), 1378
  17. Luo, L., et al., Design of a Combined Wind Speed Forecasting System Based on Decomposition-Ensemble and Multi-Objective Optimization Approach, Applied Mathematical Modelling, 89 (2021), Part 1, pp. 49-72
  18. Jia, Y., et al., Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method, Journal of Advanced Transportation, 2017 (2017), ID6575947
  19. Shabani, A., A New Optimization Algorithm Based on Search and Rescue Operations, Mathematical Problems in Engineering, 2019 (2019), ID2482543
  20. Shabani, A., et al., Search and Rescue Optimization Algorithm: A New Optimization Method for Solving Constrained Engineering Optimization Problems, Expert Systems with Applications, 161 (2020), 113698
  21. Trebing, K., Mehrkanoon, S., Wind Speed Prediction Using Multidimensional Convolutional Neural Networks, Proceeding, IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020, pp. 713-720
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Short term wind speed prediction using search and rescue optimization with deep belief network on scatterometry data

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence