@misc{10481/106464,
year = {2025},
month = {8},
url = {https://hdl.handle.net/10481/106464},
abstract = {Accurate photovoltaic (PV) power forecasting is essential for grid integration, particularly in
maritime climates with dynamic weather patterns. This study addresses high-dimensional
meteorological data challenges by systematically evaluating 32 variables across four categories (solar irradiance, temperature, atmospheric, hydrometeorological) for day-ahead PV
forecasting using long short-term memory (LSTM) networks. Using six years of data from
a 350 kWp solar farm in Scotland, we compare satellite-derived data and local weather
station measurements. Surprisingly, downward thermal infrared flux—capturing persistent atmospheric moisture and cloud properties in maritime climates—emerged as the
most influential predictor despite low correlation (1.93%). When paired with precipitation
data, this two-variable combination achieved 99.81% R2
, outperforming complex multivariable models. Satellite data consistently surpassed ground measurements, with 9 of the
top 10 predictors being satellite derived. Our approach reduces model complexity while
improving forecasting accuracy, providing practical solutions for energy systems.},
publisher = {MDPI},
keywords = {Deep learning},
keywords = {Forecasting},
keywords = {Long short-term memory},
title = {Deep Feature Selection of Meteorological Variables for LSTM-Based PV Power Forecasting in High-Dimensional Time-Series Data},
doi = {10.3390/a18080496},
author = {Mauladdawilah, Husein and Balfaqih, Mohammed and Balfagih, Zain and Pegalajar Jiménez, María Del Carmen and Jadraque Gago, Eulalia},
}