Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation
Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predict...
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2025-06-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
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| Online Access: | https://www.ijimai.org/journal/bibcite/reference/3391 |
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| author | Miguel Martínez-Comesaña Javier Martínez-Torres Pablo Eguía-Oller Javier López-Gómez |
| author_facet | Miguel Martínez-Comesaña Javier Martínez-Torres Pablo Eguía-Oller Javier López-Gómez |
| author_sort | Miguel Martínez-Comesaña |
| collection | DOAJ |
| description | Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse. |
| format | Article |
| id | doaj-art-6651970016f945658c09e176fc856e7b |
| institution | OA Journals |
| issn | 1989-1660 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj-art-6651970016f945658c09e176fc856e7b2025-08-20T02:23:56ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602025-06-0193617010.9781/ijimai.2023.11.002ijimai.2023.11.002Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV GenerationMiguel Martínez-ComesañaJavier Martínez-TorresPablo Eguía-OllerJavier López-GómezOptimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse.https://www.ijimai.org/journal/bibcite/reference/3391genetic algorithmslstmoptimisationpre-trainingpv powersynthetic datasets |
| spellingShingle | Miguel Martínez-Comesaña Javier Martínez-Torres Pablo Eguía-Oller Javier López-Gómez Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation International Journal of Interactive Multimedia and Artificial Intelligence genetic algorithms lstm optimisation pre-training pv power synthetic datasets |
| title | Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation |
| title_full | Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation |
| title_fullStr | Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation |
| title_full_unstemmed | Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation |
| title_short | Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation |
| title_sort | use of optimised lstm neural networks pre trained with synthetic data to estimate pv generation |
| topic | genetic algorithms lstm optimisation pre-training pv power synthetic datasets |
| url | https://www.ijimai.org/journal/bibcite/reference/3391 |
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