Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)
Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. T...
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| Format: | Article |
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MDPI
2019
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| author | Abdullah, Nor Azliana Rahim, Nasrudin Abd Gan, Chin Kim Nor Adzman, Noriah |
| author_facet | Abdullah, Nor Azliana Rahim, Nasrudin Abd Gan, Chin Kim Nor Adzman, Noriah |
| author_sort | Abdullah, Nor Azliana |
| collection | UM |
| description | Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data. © 2019 by the authors. |
| first_indexed | 2024-03-06T06:02:17Z |
| format | Article |
| id | um.eprints-24269 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:02:17Z |
| publishDate | 2019 |
| publisher | MDPI |
| record_format | dspace |
| spelling | um.eprints-242692020-05-05T04:47:48Z http://eprints.um.edu.my/24269/ Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS) Abdullah, Nor Azliana Rahim, Nasrudin Abd Gan, Chin Kim Nor Adzman, Noriah TK Electrical engineering. Electronics Nuclear engineering Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data. © 2019 by the authors. MDPI 2019 Article PeerReviewed Abdullah, Nor Azliana and Rahim, Nasrudin Abd and Gan, Chin Kim and Nor Adzman, Noriah (2019) Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS). Applied Sciences, 9 (16). p. 3214. ISSN 2076-3417, DOI https://doi.org/10.3390/app9163214 <https://doi.org/10.3390/app9163214>. https://doi.org/10.3390/app9163214 doi:10.3390/app9163214 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Abdullah, Nor Azliana Rahim, Nasrudin Abd Gan, Chin Kim Nor Adzman, Noriah Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS) |
| title | Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS) |
| title_full | Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS) |
| title_fullStr | Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS) |
| title_full_unstemmed | Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS) |
| title_short | Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS) |
| title_sort | forecasting solar power using hybrid firefly and particle swarm optimization hfpso for optimizing the parameters in a wavelet transform adaptive neuro fuzzy inference system wt anfis |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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