Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?

• URL: http://arxiv.org/abs/2211.02108v1
• Date: Thu, 3 Nov 2022 19:25:28 GMT
• Title: Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?
• Authors: Yuhao Nie, Quentin Paletta, Andea Scotta, Luis Martin Pomares, Guillaume Arbod, Sgouris Sgouridis, Joan Lasenby, Adam Brandt
• Abstract summary: One of the biggest challenges for training deep learning models is the availability of labeled datasets. With more and more sky image datasets open sourced in recent years, the development of accurate and reliable solar forecasting methods has seen a huge growth in potential.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Solar forecasting from ground-based sky images using deep learning models has shown great promise in reducing the uncertainty in solar power generation. One of the biggest challenges for training deep learning models is the availability of labeled datasets. With more and more sky image datasets open sourced in recent years, the development of accurate and reliable solar forecasting methods has seen a huge growth in potential. In this study, we explore three different training strategies for deep-learning-based solar forecasting models by leveraging three heterogeneous datasets collected around the world with drastically different climate patterns. Specifically, we compare the performance of models trained individually based on local datasets (local models) and models trained jointly based on the fusion of multiple datasets from different locations (global models), and we further examine the knowledge transfer from pre-trained solar forecasting models to a new dataset of interest (transfer learning models). The results suggest that the local models work well when deployed locally, but significant errors are observed for the scale of the prediction when applied offsite. The global model can adapt well to individual locations, while the possible increase in training efforts need to be taken into account. Pre-training models on a large and diversified source dataset and transferring to a local target dataset generally achieves superior performance over the other two training strategies. Transfer learning brings the most benefits when there are limited local data. With 80% less training data, it can achieve 1% improvement over the local baseline model trained using the entire dataset. Therefore, we call on the efforts from the solar forecasting community to contribute to a global dataset containing a massive amount of imagery and displaying diversified samples with a range of sky conditions.

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