A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting
Efficient and accurate prediction of renewable energy sources (RES) is an interminable challenge in efforts to assure the stable and safe operation of any hybrid energy system due to its intermittent nature. High integration of RES especially wind energy into the existing power sector in recent year...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023004012 |
| _version_ | 1797682453995847680 |
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| author | S. Syama J. Ramprabhakar R. Anand Josep M. Guerrero |
| author_facet | S. Syama J. Ramprabhakar R. Anand Josep M. Guerrero |
| author_sort | S. Syama |
| collection | DOAJ |
| description | Efficient and accurate prediction of renewable energy sources (RES) is an interminable challenge in efforts to assure the stable and safe operation of any hybrid energy system due to its intermittent nature. High integration of RES especially wind energy into the existing power sector in recent years has made the situation still challenging which draws the attention of many researchers in developing a computationally efficient forecast model for accurately predicting RES. With the advent of Neural network based methods, ELM -Extreme Learning Machine, a typical Single Layer Feedforward Network (SLFFN), has gained a significant attention in recent years in solving various real-time complex problems due to simplified architecture, good generalization capabilities and fast computation. However, since the model parameters are randomly assigned, the conventional ELM is frequently ranked as the second-best model. As a solution, the article attempts to construct a unique optimized Extreme Learning Machine (ELM) based forecast model with improved accuracy for wind speed forecasting. A novel swarm intelligence technique- Lévy flight Chaotic Whale Optimization algorithm (LCWOA) is utilized in the hybrid model to optimize different parameters of ELM. Despite having a appropriate convergence rate, WOA is occasionally unable to discover the global optima due to imbalanced exploration and exploitation when using control parameters with linear variation. An improvement in the convergence rate of WOA can be expected by incorporating chaotic maps in the control parameters of WOA due to their ergodic nature. In addition to this, Lévy flight can significantly improve the intensification and diversification of the Whale Optimization algorithm (WOA) resulting in improvised search ability avoiding local minima. The prediction capability of the suggested hybrid Extreme Learning Machine (ELM) based forecast model is validated with nine other existing models. The experimental study affirms that the suggested model outperform existing forecasting methods in a variety of quantitative metrics. |
| first_indexed | 2024-03-11T23:59:56Z |
| format | Article |
| id | doaj.art-3eade30e7b3244b282ed9ccba2c4b422 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-11T23:59:56Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-3eade30e7b3244b282ed9ccba2c4b4222023-09-18T04:30:36ZengElsevierResults in Engineering2590-12302023-09-0119101274A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed ForecastingS. Syama0J. Ramprabhakar1R. Anand2Josep M. Guerrero3Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India; Corresponding author. Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru Campus, Kasavanahalli, Carmelaram P.O, Bengaluru, 560 035, Karnataka, India.Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, IndiaDepartment of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, IndiaDepartment of Energy Technology, Aalborg University, Aalborg East, 9220, DenmarkEfficient and accurate prediction of renewable energy sources (RES) is an interminable challenge in efforts to assure the stable and safe operation of any hybrid energy system due to its intermittent nature. High integration of RES especially wind energy into the existing power sector in recent years has made the situation still challenging which draws the attention of many researchers in developing a computationally efficient forecast model for accurately predicting RES. With the advent of Neural network based methods, ELM -Extreme Learning Machine, a typical Single Layer Feedforward Network (SLFFN), has gained a significant attention in recent years in solving various real-time complex problems due to simplified architecture, good generalization capabilities and fast computation. However, since the model parameters are randomly assigned, the conventional ELM is frequently ranked as the second-best model. As a solution, the article attempts to construct a unique optimized Extreme Learning Machine (ELM) based forecast model with improved accuracy for wind speed forecasting. A novel swarm intelligence technique- Lévy flight Chaotic Whale Optimization algorithm (LCWOA) is utilized in the hybrid model to optimize different parameters of ELM. Despite having a appropriate convergence rate, WOA is occasionally unable to discover the global optima due to imbalanced exploration and exploitation when using control parameters with linear variation. An improvement in the convergence rate of WOA can be expected by incorporating chaotic maps in the control parameters of WOA due to their ergodic nature. In addition to this, Lévy flight can significantly improve the intensification and diversification of the Whale Optimization algorithm (WOA) resulting in improvised search ability avoiding local minima. The prediction capability of the suggested hybrid Extreme Learning Machine (ELM) based forecast model is validated with nine other existing models. The experimental study affirms that the suggested model outperform existing forecasting methods in a variety of quantitative metrics.http://www.sciencedirect.com/science/article/pii/S2590123023004012Wind speed forecastingExtreme learning machinesWhale optimization algorithmLévy flight, Chaotic OptimizationRecurssive prediction |
| spellingShingle | S. Syama J. Ramprabhakar R. Anand Josep M. Guerrero A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting Results in Engineering Wind speed forecasting Extreme learning machines Whale optimization algorithm Lévy flight, Chaotic Optimization Recurssive prediction |
| title | A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting |
| title_full | A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting |
| title_fullStr | A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting |
| title_full_unstemmed | A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting |
| title_short | A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting |
| title_sort | hybrid extreme learning machine model with levy flight chaotic whale optimization algorithm for wind speed forecasting |
| topic | Wind speed forecasting Extreme learning machines Whale optimization algorithm Lévy flight, Chaotic Optimization Recurssive prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023004012 |
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