TY - JOUR
KW - Genetic Algorithms
KW - LSTM
KW - Optimisation
KW - Pre-Training
KW - PV Power
KW - Synthetic Datasets
AU - Miguel Martínez-Comesaña
AU - Javier Martínez-Torres
AU - Pablo Eguía-Oller
AU - Javier López-Gómez
AB - 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.
IS - In press
M1 - In press
N2 - 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.
PY - 9998
SE - 1
SP - 1
EP - 10
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation
UR - https://www.ijimai.org/journal/sites/default/files/2023-11/ip2023_11_002_1.pdf
VL - In press
SN - 1989-1660
ER -