Probabilistic end-to-end irradiance forecasting through pre-trained deep learning models using all-sky-images
International Centre for Sustainable Development (IZNE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
Sebastian Houben
Institute for Artificial Intelligence and Autonomous Systems (A2S), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
Stefanie Meilinger
International Centre for Sustainable Development (IZNE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
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Atmos. Meas. Tech., 16, 4975–5007, https://doi.org/10.5194/amt-16-4975-2023, https://doi.org/10.5194/amt-16-4975-2023, 2023
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Measured power data from solar photovoltaic (PV) systems contain information about the state of the atmosphere. In this work, power data from PV systems in the Allgäu region in Germany were used to determine the solar irradiance at each location, using state-of-the-art simulation and modelling. The results were validated using concurrent measurements of the incoming solar radiation in each case. If applied on a wider scale, this algorithm could help improve weather and climate models.
Jonas Witthuhn, Anja Hünerbein, Florian Filipitsch, Stefan Wacker, Stefanie Meilinger, and Hartwig Deneke
Atmos. Chem. Phys., 21, 14591–14630, https://doi.org/10.5194/acp-21-14591-2021, https://doi.org/10.5194/acp-21-14591-2021, 2021
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Knowledge of aerosol–radiation interactions is important for understanding the climate system and for the renewable energy sector. Here, two complementary approaches are used to assess the consistency of the underlying aerosol properties and the resulting radiative effect in clear-sky conditions over Germany in 2015. An approach based on clear-sky models and broadband irradiance observations is contrasted to the use of explicit radiative transfer simulations using CAMS reanalysis data.
Ina Neher, Susanne Crewell, Stefanie Meilinger, Uwe Pfeifroth, and Jörg Trentmann
Atmos. Chem. Phys., 20, 12871–12888, https://doi.org/10.5194/acp-20-12871-2020, https://doi.org/10.5194/acp-20-12871-2020, 2020
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Photovoltaic power is one current option to meet the rising energy demand with low environmental impact. Global horizontal irradiance (GHI) is the fuel for photovoltaic power installations and needs to be evaluated to plan and dimension power plants. In this study, 35 years of satellite-based GHI data are analyzed over West Africa to determine their impact on photovoltaic power generation. The major challenges for the development of a solar-based power system in West Africa are then outlined.
James Barry, Dirk Böttcher, Klaus Pfeilsticker, Anna Herman-Czezuch, Nicola Kimiaie, Stefanie Meilinger, Christopher Schirrmeister, Hartwig Deneke, Jonas Witthuhn, and Felix Gödde
Adv. Sci. Res., 17, 165–173, https://doi.org/10.5194/asr-17-165-2020, https://doi.org/10.5194/asr-17-165-2020, 2020
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The power output of solar photovoltaic (PV) modules depends largely upon incident solar radiation as well as PV module temperature. Although irradiance can fluctuate rapidly under broken cloud conditions, module temperature is subject to latency due to the solar panel's heat capacity. In order to reconcile this difference a simple four-parameter model is successfully employed to describe the dynamics of PV module temperature as a function of atmospheric conditions.
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Short summary
This method generates probabilistic solar power forecasts of up to 30 min using artificial intelligence and fish eye images of the sky. We investigated the impact of parameters describing the variability of the solar power on the forecast performance and divided the solar power to its direct and diffuse components. The method achieves overall good performance, while variability parameters and dividing the solar power don't show any performance gain. However, training time can be decreased.
This method generates probabilistic solar power forecasts of up to 30 min using artificial...