Semi-Supervised Deep Domain Adaptation for Predicting Solar Power Across Different Locations

• URL: http://arxiv.org/abs/2508.04165v1
• Date: Wed, 06 Aug 2025 07:45:35 GMT
• Title: Semi-Supervised Deep Domain Adaptation for Predicting Solar Power Across Different Locations
• Authors: Md Shazid Islam, A S M Jahid Hasan, Md Saydur Rahman, Md Saiful Islam Sajol,
• Abstract summary: This paper presents a semi-supervised deep domain adaptation framework, allowing accurate predictions with minimal labeled data from the target location.<n>Our approach involves training a deep convolutional neural network on a source location's data and adapting it to the target location using a source-free, teacher-student model configuration.<n>With annotation of only $20 %$ data in the target domain, our approach exhibits an improvement upto $11.36 %$, $6.65 %$, $4.92%$ for California, Florida and New York as target domain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate solar generation prediction is essential for proper estimation of renewable energy resources across diverse geographic locations. However, geographical and weather features vary from location to location which introduces domain shift - a major bottleneck to develop location-agnostic prediction model. As a result, a machine-learning model which can perform well to predict solar power in one location, may exhibit subpar performance in another location. Moreover, the lack of properly labeled data and storage issues make the task even more challenging. In order to address domain shift due to varying weather conditions across different meteorological regions, this paper presents a semi-supervised deep domain adaptation framework, allowing accurate predictions with minimal labeled data from the target location. Our approach involves training a deep convolutional neural network on a source location's data and adapting it to the target location using a source-free, teacher-student model configuration. The teacher-student model leverages consistency and cross-entropy loss for semi-supervised learning, ensuring effective adaptation without any source data requirement for prediction. With annotation of only $20 \%$ data in the target domain, our approach exhibits an improvement upto $11.36 \%$, $6.65 \%$, $4.92\%$ for California, Florida and New York as target domain, respectively in terms of accuracy in predictions with respect to non-adaptive approach.

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