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Wei Zhuang, Zhiheng Li, Ying Wang, Qingyu Xi and Min Xia
Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction...
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Pavel V. Matrenin, Valeriy V. Gamaley, Alexandra I. Khalyasmaa and Alina I. Stepanova
Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance ...
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Haizhou Cao, Jing Yang, Xuemeng Zhao, Tiechui Yao, Jue Wang, Hui He and Yangang Wang
The penetration of photovoltaic (PV) energy has gained a significant increase in recent years because of its sustainable and clean characteristics. However, the uncertainty of PV power affected by variable weather poses challenges to an accurate short-te...
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Joseph Ndong and Ted Soubdhan
Building a sophisticated forecasting framework for solar and photovoltaic power production in geographic zones with severe meteorological conditions is very challenging. This difficulty is linked to the high variability of the global solar radiation on w...
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Seyed Mahdi Miraftabzadeh, Cristian Giovanni Colombo, Michela Longo and Federica Foiadelli
Climate change and global warming drive many governments and scientists to investigate new renewable and green energy sources. Special attention is on solar panel technology, since solar energy is considered one of the primary renewable sources and solar...
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Georgios Venitourakis, Christoforos Vasilakis, Alexandros Tsagkaropoulos, Tzouma Amrou, Georgios Konstantoulakis, Panagiotis Golemis and Dionysios Reisis
Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, whi...
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Everton Jose Santana, Ricardo Petri Silva, Bruno Bogaz Zarpelão and Sylvio Barbon Junior
With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular...
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Abdelkader Dairi, Fouzi Harrou, Ying Sun and Sofiane Khadraoui
The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar p...
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Stanislav A. Eroshenko, Alexandra I. Khalyasmaa, Denis A. Snegirev, Valeria V. Dubailova, Alexey M. Romanov and Denis N. Butusov
The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days? accurate and robust power generation forecasting. We provide a brief description of the piloted short-...
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Youngil Kim, Keunjoo Seo, Robert J. Harrington, Yongju Lee, Hyeok Kim and Sungjin Kim
More accurate self-forecasting not only provides a better-integrated solution for electricity grids but also reduces the cost of operation of the entire power system. To predict solar photovoltaic (PV) power generation (SPVG) for a specific hour, this pa...
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