Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Denslin Brabin, D.R.
- Whale Swarm Optimization Based ANFIS for Prediction in Forecasting Application
Abstract Views :203 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Kristu Jayanti College, IN
2 School of Engineering and Technology, Vivekananda Institute of Professional Studies Technical Campus, IN
3 Department of Computer Science and Engineering, DMI College of Engineering, IN
4 Department of Electronics and Communication Engineering, Rajiv Gandhi University of Knowledge Technologies, IN
1 Department of Computer Science, Kristu Jayanti College, IN
2 School of Engineering and Technology, Vivekananda Institute of Professional Studies Technical Campus, IN
3 Department of Computer Science and Engineering, DMI College of Engineering, IN
4 Department of Electronics and Communication Engineering, Rajiv Gandhi University of Knowledge Technologies, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 3 (2024), Pagination: 3237-3242Abstract
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
- Hu Xiaohui and R. Eberhart, “Multiobjective Optimization using Dynamic Neighborhood Particle Swarm Optimization”, Proceedings of Congress on Evolutionary Computation, Vol. 2, pp. 1677-1681, 2002.
- R. Padma Sree and M. Chidambaram, “Control of Unstable Systems”, Alpha Science, 2005.
- D.T. Pham and D. Karaboga, “Intelligent Optimization Techniques”, Springer, 2000.
- H. Liu and H. Motoda, “Feature Selection for Knowledge Discovery and Data Mining”, Kluwer Academic Publishers, 2012.
- S. Kalaivani and Gopinath Ganapath, “Bio-Inspired Modified Bees Colony Feature Selection based Intrusion Detection System for Cloud Computing Application”, International Journal of Advanced Science and Technology, Vol. 29, No. 3, pp. 1-12, 2020.
- G.L. Stavrinides and H.D. Karatza, “Scheduling Multiple Task Graphs with End-to-End Deadlines in Distributed Real-Time Systems Utilizing Imprecise Computations”, Journal of Systems and Software, Vol. 83, No. 6, pp. 1004-1014, 2010.
- S. Kannan, C. Selvaraj and S.N. Mohanty, “Forecasting Energy Generation in Large Photovoltaic Plants using Radial Belief Neural Network”, Sustainable Computing: Informatics and Systems, Vol. 31, pp. 100578-100589, 2021.
- S. Karthick and P.A. Rajakumari, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering Using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.
- M. Mohseni, P. Raturi and S.J. Priya, “The Role of Parallel Computing Towards Implementation of Enhanced and Effective Industrial Internet of Things (IOT) Through Manova Approach”, Proceedings of International Conference on Advance Computing and Innovative Technologies in Engineering, pp. 160-164, 2022.
- G. Perveen and N. Goel, “An ANFIS‐Based Model for Solar Energy Forecasting and its Smart Grid Application”, Engineering Reports, Vol. 1, No. 5, pp. 1-13, 2019.
- M. Viswavandya and A. Mohanty, “Short Term Solar Energy Forecasting by using Fuzzy Logic and ANFIS”, Proceedings of the International Conference on Computational Intelligence in Data Mining, pp. 751-765, 2020.
- H. Suyono and I. Musirin, “Comparison of Solar Radiation Intensity Forecasting using ANFIS and Multiple Linear Regression Methods”, Bulletin of Electrical Engineering and Informatics, Vol. 7, No. 2, pp. 191-198, 2018.