Related papers: Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar Power Generation

Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar Power Generation

URL: http://arxiv.org/abs/2401.14422v2
Date: Tue, 6 Feb 2024 08:31:40 GMT
Title: Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar Power Generation
Authors: Md Shazid Islam, A S M Jahid Hasan, Md Saydur Rahman, Jubair Yusuf, Md Saiful Islam Sajol, Farhana Akter Tumpa
Abstract summary: This paper proposes a domain adaptive deep learning-based framework to estimate solar power generation using weather features. A feed-forward deep convolutional network model is trained for a known location dataset in a supervised manner and utilized to predict the solar power of an unknown location later. Our method has shown an improvement of $10.47 %$, $7.44 %$, $5.11%$ in solar power prediction accuracy compared to best performing non-adaptive method for California (CA), Florida (FL) and New York (NY), respectively.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: The prediction of solar power generation is a challenging task due to its dependence on climatic characteristics that exhibit spatial and temporal variability. The performance of a prediction model may vary across different places due to changes in data distribution, resulting in a model that works well in one region but not in others. Furthermore, as a consequence of global warming, there is a notable acceleration in the alteration of weather patterns on an annual basis. This phenomenon introduces the potential for diminished efficacy of existing models, even within the same geographical region, as time progresses. In this paper, a domain adaptive deep learning-based framework is proposed to estimate solar power generation using weather features that can solve the aforementioned challenges. A feed-forward deep convolutional network model is trained for a known location dataset in a supervised manner and utilized to predict the solar power of an unknown location later. This adaptive data-driven approach exhibits notable advantages in terms of computing speed, storage efficiency, and its ability to improve outcomes in scenarios where state-of-the-art non-adaptive methods fail. Our method has shown an improvement of $10.47 \%$, $7.44 \%$, $5.11\%$ in solar power prediction accuracy compared to best performing non-adaptive method for California (CA), Florida (FL) and New York (NY), respectively.

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