Deep learning for single-site solar irradiance forecasting using multi-station data
This study examines the integration of data from multiple stations for solar irradiance forecasting at a single site using advanced deep learning models, such as long-term memory (LSTM), deep modular attention (DeepMap), and graph convolutional networks (GC-LSTM). The research addresses an important...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | Environmental Research Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2515-7620/adaf79 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This study examines the integration of data from multiple stations for solar irradiance forecasting at a single site using advanced deep learning models, such as long-term memory (LSTM), deep modular attention (DeepMap), and graph convolutional networks (GC-LSTM). The research addresses an important gap: the statistical evaluation of the contribution of neighboring data to improving forecast accuracy in solar PV applications. Using a large dataset from 12 Colombian locations, representing diverse climatic conditions, we rigorously evaluate the ability of these models to take advantage of spatio-temporal information. The results reveal slight improvements in short-term forecasting, but these improvements are statistically insignificant, as validated by chi-square tests. The results highlight the need for more advanced methods to effectively exploit spatial data, which will guide the future development of solar irradiance prediction models. |
|---|---|
| ISSN: | 2515-7620 |