Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization
Regardless of their nature of stochasticity and uncertain nature, wind and solar resources are the most abundant energy resources used in the development of microgrid systems. In microgrid systems and distribution networks, the uncertain nature of both solar and wind resources results in power quali...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023000099 |
| _version_ | 1811173380649385984 |
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| author | Demsew Mitiku Teferra Livingstone M.H. Ngoo George N. Nyakoe |
| author_facet | Demsew Mitiku Teferra Livingstone M.H. Ngoo George N. Nyakoe |
| author_sort | Demsew Mitiku Teferra |
| collection | DOAJ |
| description | Regardless of their nature of stochasticity and uncertain nature, wind and solar resources are the most abundant energy resources used in the development of microgrid systems. In microgrid systems and distribution networks, the uncertain nature of both solar and wind resources results in power quality and system stability issues. The randomization behavior of solar and wind energy resources is controlled through the precise development of a power prediction model. Fuzzy-based solar PV and wind prediction models may more efficiently manage this randomness and uncertain character. However, this method has several drawbacks, it has limited performance when the volumes of wind and solar resources historical data are huge in size and it has also many membership functions of the fuzzy input and output variables as well as multiple fuzzy rules available. The hybrid Fuzzy-PSO intelligent prediction approach improves the fuzzy system's limitations and hence increases the prediction model's performance. The Fuzzy-PSO hybrid forecast model is developed using MATLAB programming of the particle swarm optimization (PSO) algorithm with the help of the global optimization toolbox. In this paper, an error correction factor (ECF) is considered a new fuzzy input variable. It depends on the validation and forecasted data values of both wind and solar prediction models to improve the accuracy of the prediction model. The impact of ECF is observed in fuzzy, Fuzzy-PSO, and Fuzzy-GA wind and solar PV power forecasting models. The hybrid Fuzzy-PSO prediction model of wind and solar power generation has a high degree of accuracy compared to the Fuzzy and Fuzzy-GA forecasting models.The rest of this paper is organized as: Section II is about the analysis of solar and wind resources row data. The Fuzzy-PSO prediction model problem formulation is covered in Section III. Section IV, is about the results and discussion of the study. Section V contains the conclusion. The references and abbreviations are presented at the end of the paper. |
| first_indexed | 2024-04-10T17:46:57Z |
| format | Article |
| id | doaj.art-dc96ef7b7c064627a77cde4b7706b1d1 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-10T17:46:57Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-dc96ef7b7c064627a77cde4b7706b1d12023-02-03T04:59:10ZengElsevierHeliyon2405-84402023-01-0191e12802Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimizationDemsew Mitiku Teferra0Livingstone M.H. Ngoo1George N. Nyakoe2Pan-African University Institute of Basic Science, Technology and Innovation, Nairobi, Kenya; Corresponding author.Department of Electrical & Communications Engineering, Multimedia University of Kenya, Nairobi, Kenya; Corresponding author.,Department of Electrical Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya; Corresponding author.Regardless of their nature of stochasticity and uncertain nature, wind and solar resources are the most abundant energy resources used in the development of microgrid systems. In microgrid systems and distribution networks, the uncertain nature of both solar and wind resources results in power quality and system stability issues. The randomization behavior of solar and wind energy resources is controlled through the precise development of a power prediction model. Fuzzy-based solar PV and wind prediction models may more efficiently manage this randomness and uncertain character. However, this method has several drawbacks, it has limited performance when the volumes of wind and solar resources historical data are huge in size and it has also many membership functions of the fuzzy input and output variables as well as multiple fuzzy rules available. The hybrid Fuzzy-PSO intelligent prediction approach improves the fuzzy system's limitations and hence increases the prediction model's performance. The Fuzzy-PSO hybrid forecast model is developed using MATLAB programming of the particle swarm optimization (PSO) algorithm with the help of the global optimization toolbox. In this paper, an error correction factor (ECF) is considered a new fuzzy input variable. It depends on the validation and forecasted data values of both wind and solar prediction models to improve the accuracy of the prediction model. The impact of ECF is observed in fuzzy, Fuzzy-PSO, and Fuzzy-GA wind and solar PV power forecasting models. The hybrid Fuzzy-PSO prediction model of wind and solar power generation has a high degree of accuracy compared to the Fuzzy and Fuzzy-GA forecasting models.The rest of this paper is organized as: Section II is about the analysis of solar and wind resources row data. The Fuzzy-PSO prediction model problem formulation is covered in Section III. Section IV, is about the results and discussion of the study. Section V contains the conclusion. The references and abbreviations are presented at the end of the paper.http://www.sciencedirect.com/science/article/pii/S2405844023000099Fuzzy-GA Hybrid algorithmFuzzy-PSO AlgorithmFuzzy systemParticle swarm optimizationSolar power prediction modelWind power prediction model |
| spellingShingle | Demsew Mitiku Teferra Livingstone M.H. Ngoo George N. Nyakoe Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization Heliyon Fuzzy-GA Hybrid algorithm Fuzzy-PSO Algorithm Fuzzy system Particle swarm optimization Solar power prediction model Wind power prediction model |
| title | Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization |
| title_full | Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization |
| title_fullStr | Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization |
| title_full_unstemmed | Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization |
| title_short | Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization |
| title_sort | fuzzy based prediction of solar pv and wind power generation for microgrid modeling using particle swarm optimization |
| topic | Fuzzy-GA Hybrid algorithm Fuzzy-PSO Algorithm Fuzzy system Particle swarm optimization Solar power prediction model Wind power prediction model |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023000099 |
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