ICTACT Journal on Soft Computing

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Whale Swarm Optimization Based ANFIS for Prediction in Forecasting Application


Affiliations
1 Department of Computer Science, Kristu Jayanti College, India
2 School of Engineering and Technology, Vivekananda Institute of Professional Studies Technical Campus, India
3 Department of Computer Science and Engineering, DMI College of Engineering, India
4 Department of Electronics and Communication Engineering, Rajiv Gandhi University of Knowledge Technologies, India
     

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This paper proposes a novel approach for solar power forecasting using an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with Whale Swarm Optimization (WSO). The synergy between ANFIS and WSO aims to overcome the limitations of traditional forecasting models by controlling the collective intelligence of a whale-inspired swarm algorithm. The WSO optimizes the parameters of the ANFIS, leading to improved accuracy in solar power predictions. The integration of WSO introduces a parallelism and exploration-exploitation balance inspired by the natural behaviors of whales, enhancing the ANFIS model’s adaptability to dynamic solar power generation patterns. The experimental results demonstrate the superiority of the proposed Whale Swarm-based ANFIS over conventional methods, showcasing its ability to handle non-linear and complex relationships in solar power data. This research contributes to the field of renewable energy forecasting by presenting an innovative hybrid model that leverages the strengths of both ANFIS and WSO for more reliable and precise solar power predictions.

Keywords

Whale Swarm Optimization, ANFIS, Solar Power Forecasting, Renewable Energy, Hybrid Model.
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  • Whale Swarm Optimization Based ANFIS for Prediction in Forecasting Application

Abstract Views: 26  |  PDF Views: 0

Authors

A. Sevuga Pandian
Department of Computer Science, Kristu Jayanti College, India
Deepali Virmani
School of Engineering and Technology, Vivekananda Institute of Professional Studies Technical Campus, India
D.R. Denslin Brabin
Department of Computer Science and Engineering, DMI College of Engineering, India
Sk. Riyaz Hussain
Department of Electronics and Communication Engineering, Rajiv Gandhi University of Knowledge Technologies, India

Abstract


This paper proposes a novel approach for solar power forecasting using an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with Whale Swarm Optimization (WSO). The synergy between ANFIS and WSO aims to overcome the limitations of traditional forecasting models by controlling the collective intelligence of a whale-inspired swarm algorithm. The WSO optimizes the parameters of the ANFIS, leading to improved accuracy in solar power predictions. The integration of WSO introduces a parallelism and exploration-exploitation balance inspired by the natural behaviors of whales, enhancing the ANFIS model’s adaptability to dynamic solar power generation patterns. The experimental results demonstrate the superiority of the proposed Whale Swarm-based ANFIS over conventional methods, showcasing its ability to handle non-linear and complex relationships in solar power data. This research contributes to the field of renewable energy forecasting by presenting an innovative hybrid model that leverages the strengths of both ANFIS and WSO for more reliable and precise solar power predictions.

Keywords


Whale Swarm Optimization, ANFIS, Solar Power Forecasting, Renewable Energy, Hybrid Model.

References