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Photovoltaic Output...
Photovoltaic Output Potential Assessment via Transformer-based Solar Forecasting and Rooftop Segmentation Methods
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- Gong, Y. (författare)
- Sichuan University, China
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- Guo, Z. (författare)
- The Hong Kong Polytechnic University, Hong Kong
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- Li, X. (författare)
- BeiJIng University of Chemical Technology, China
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- Shi, Xiaodan (författare)
- Mälardalens universitet,Framtidens energi,The University of Tokyo, China
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- Lin, Z. (författare)
- The Hong Kong Polytechnic University, Hong Kong
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- Zhang, H. (författare)
- Peking University, China
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- Yan, Jinyue, 1959- (författare)
- The Hong Kong Polytechnic University, Hong Kong
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(creator_code:org_t)
- Scanditale AB, 2023
- 2023
- Engelska.
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Ingår i: Energy Proceedings. - : Scanditale AB.
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.4...
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Abstract
Ämnesord
Stäng
- Given the escalating carbon emission crisis, there is an urgent need for large-scale adoption of renewable energy generation to replace traditional fossil fuelbased energy generation for a smooth energy transition. In this regard, distributed photovoltaic power generation plays a crucial role. Predicting the GHI in advance to predict the power of photovoltaic power generation has become one of the methods to solve the grid-connected stability in recent years, which enables the grid staff to dispatch and plan in advance through the forecast results, reduce fluctuations, and maintain grid stability. In this study, we present a deep learningbased method to assess photovoltaic output potential by solar irradiance forecasting and rooftop segmentation. First, we utilize a multivariate input Transformer model that incorporates various data to predict GHI; Second, using remote sensing images to train Swin-Transformer to identify the potential area of rooftop photovoltaic panel; Finally, the potential assessment was achieved by calculating the array output through the GHI and area data we generated in the first two parts. Our evaluation methodology and results provide technical support for the transition of energy structure.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Nyckelord
- deep learning
- photovoltaic potential
- renewable energy
- segmentation
- solar forecasting
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)