SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting

TL;DR

SolarTformer is an attention-based deep learning model that improves short-term solar power forecasting by capturing temporal and spatial dependencies, incorporating station-specific metadata, and outperforming previous models.

Contribution

The paper introduces SolarTformer, a transformer-inspired model that enhances solar power prediction accuracy by leveraging self-attention and metadata integration.

Findings

SolarTformer significantly outperforms previous models on the same dataset.

The model performs well on both clear and cloudy days, showing robustness.

Incorporating metadata improves generalization across different locations and seasons.

Abstract

Accurate forecasting of solar power output is essential for efficient integration of renewable energy into the grid. In this study, an attention-based deep learning model, inspired by transformer architecture, is used for short-term solar power forecasting. Our proposed model, "SolarTformer", is designed to predict solar power output from meteorological data. Unlike traditional models, SolarTformer leverages self-attention mechanisms to effectively capture temporal dependencies and spatial variability in solar irradiance. In addition, the proposed methodology includes feeding power station-specific metadata into the model, which helps to generalize between power stations located at different locations and with different panel configurations and in different seasons. Our experiments demonstrate that SolarTformer significantly outperforms previous models on the same data set. In particular, the model exhibits strong performance on both clear and cloudy days, indicating high robustness and generalizability. These findings highlight the potential of attention-based architectures in enhancing the accuracy of solar forecasting, contributing to a more reliable management of renewable energy.