Local-global methods for generalised solar irradiance forecasting
Metadatos
Mostrar el registro completo del ítemEditorial
Springer Nature
Materia
Deep learning Time series forecast Solar irradiance forecast
Fecha
2024-01-01Referencia bibliográfica
Cargan, T.R., Landa-Silva, D. & Triguero, I. Local-global methods for generalised solar irradiance forecasting. Appl Intell 54, 2225–2247 (2024). https://doi.org/10.1007/s10489-024-05273-9
Patrocinador
Projects A-TIC-434- UGR20 and PID2020-119478GB-I00; Maria Zambrano fellowship at the University of GranadaResumen
For efficient operation, solar power operators often require generation forecasts formultiple siteswith varying data availability.
Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-series,
using current observations to generate forecasts. This necessitates a real-time data stream and enough historical observations
at every location for these methods to be deployed. In this paper, we propose the use of Global methods to train generalised
models. Using data from 20 locations distributed throughout the UK, we show that it is possible to learn models without
access to data for all locations, enabling them to generate forecasts for unseen locations. We show a single Global model
trained on multiple locations can produce more consistent and accurate results across locations. Furthermore, by leveraging
weather observations and measurements from other locations we show it is possible to create models capable of accurately
forecasting irradiance at locations without any real-time data. We apply our approaches to both classical and state-of-theart
Machine Learning methods, including a Transformer architecture. We compare models using satellite imagery or point
observations (temperature, pressure, etc.) as weather data. These methods could facilitate planning and optimisation for both
newly deployed solar farms and domestic installations from the moment they come online.