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DC Field | Value | Language |
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dc.contributor.author | Wu, K | - |
dc.contributor.author | Peng, X | - |
dc.contributor.author | Li, Z | - |
dc.contributor.author | Cui, W | - |
dc.contributor.author | Yuan, H | - |
dc.contributor.author | Lai, CS | - |
dc.contributor.author | Lai, LL | - |
dc.date.accessioned | 2022-07-29T09:09:47Z | - |
dc.date.available | 2022-07-29T09:09:47Z | - |
dc.date.issued | 2022-07-27 | - |
dc.identifier.citation | Wu, K. et al. (2022) ‘A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection’, Energies, 15(15), pp. 1 - 20. doi:10.3390/en15155410. | en_US |
dc.identifier.issn | 1996-1073 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/25007 | - |
dc.description.abstract | High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models. | en_US |
dc.format.extent | 5410 - 5410 | - |
dc.format.medium | Print - Electronic | - |
dc.language | en | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | short-term PV power forecasting | en_US |
dc.subject | trend feature extraction | en_US |
dc.subject | fast correlation-based filter | en_US |
dc.subject | bidirectional long short-term memory network | en_US |
dc.title | A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.3390/en15155410 | - |
dc.relation.isPartOf | Energies | - |
pubs.issue | 15 | - |
pubs.publication-status | Published online | - |
pubs.volume | 15 | - |
dc.identifier.eissn | 1996-1073 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | 6.43 MB | Adobe PDF | View/Open |
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