01987nas a2200265   4500000000100000000000100001008004100002260001200043653002300055653000900078653001700087653001700104653001300121653002300134100003100157700002800188700002300216700002500239245010000264856008300364300000900447490001300456520123800469022001401707       9998                            d  c11/202310aGenetic Algorithms10aLSTM10aOptimisation10aPre-Training10aPV Power10aSynthetic Datasets1 aMiguel Martínez-Comesaña1 aJavier Martínez-Torres1 aPablo Eguía-Oller1 aJavier López-Gómez00aUse of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation  uhttps://www.ijimai.org/journal/sites/default/files/2023-11/ip2023_11_002_1.pdf  a1-100 vIn press3 aOptimising 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.  a1989-1660