Open Access

This dataset contains data from two measurement campaigns in autumn 2018 and summer 2019 that were part of the BMWi project "MetPVNet", and serve as a supplement to the paper "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions", published in the special edition of "Advances in Science and Research", the proceedings of the 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019. Data are resampled to one minute, and include: PV module temperature Ambient temperature Plane-of-array irradiance Windspeed Atmospheric thermal emission The data were used for the dynamic temperature model, as presented in the paper

Intention: Within the research project EnerSHelF (Energy-Self-Sufficiency for Health Facilities in Ghana), i. a. energy-meteorological and load-related measurement data are collected, for which an overview of the availability is to be presented on a poster. Context: In Ghana, the total electricity consumed has almost doubled between 2008 and 2018 according to the Energy Commission of Ghana. This goes along with an unstable power grid, resulting in power outages whenever electricity consumption peaks. The blackouts called "dumsor" in Ghana, pose a severe burden to the healthcare sector. Innovative solutions are needed to reduce greenhouse gas emissions and improve energy and health access.

The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications can benefit from forecasts with a high temporal resolution to address sudden fluctuations of PV-power. However, cloud formation processes and movements are subject to ongoing research. For now-casting applications, all-sky-imagers (ASI) are used to offer an appropriate forecasting for aforementioned application. Recent research aims to achieve these forecasts via deep learning approaches, either as an image segmentation task to generate a DNI forecast through a cloud vectoring approach to translate the DNI to a GHI with ground-based measurement (Fabel et al., 2022; Nouri et al., 2021), or as an end-to-end regression task to generate a GHI forecast directly from the images (Paletta et al., 2021; Yang et al., 2021). While end-to-end regression might be the more attractive approach for off-grid scenarios, literature reports increased performance compared to smart-persistence but do not show satisfactory forecasting patterns (Paletta et al., 2021). This work takes a step back and investigates the possibility to translate ASI-images to current GHI to deploy the neural network as a feature extractor. An ImageNet pre-trained deep learning model is used to achieve such translation on an openly available dataset by the University of California San Diego (Pedro et al., 2019). The images and measurements were collected in Folsom, California. Results show that the neural network can successfully translate ASI-images to GHI for a variety of cloud situations without the need of any external variables. Extending the neural network to a forecasting task also shows promising forecasting patterns, which shows that the neural network extracts both temporal and momentarily features within the images to generate GHI forecasts.

Integrated solar water splitting devices that produce hydrogen without the use of power inverters operate outdoors and are hence exposed to varying weather conditions. As a result, they might sometimes work at non-optimal operation points below or above the maximum power point of the photovoltaic component, which would directly translate into efficiency losses. Up until now, however, no common parameter describing and quantifying this and other real-life operating related losses (e.g. spectral mismatch) exists in the community. Therefore, the annual-hydrogen-yield-climatic-response (AHYCR) ratio is introduced as a figure of merit to evaluate the outdoor performance of integrated solar water splitting devices. This value is defined as the ratio between the real annual hydrogen yield and the theoretical yield assuming the solar-to-hydrogen device efficiency at standard conditions. This parameter is derived for an exemplary system based on state-of-the-art AlGaAs//Si dual-junction solar cells and an anion exchange membrane electrolyzer using hourly resolved climate data from a location in southern California and from reanalysis data of Antarctica. This work will help to evaluate, compare and optimize the climatic response of solar water splitting devices in different climate zones.

We examine the effect of nanometer-sized aircraft-induced aqueous sulfuric acid (H2SO4/H2O) particles on atmospheric ozone as a function of temperature. Our calculations are based on a previously derived parameterization for the regional-scale perturbations of the sulfate surface area density due to air traffic in the North Atlantic Flight Corridor (NAFC) and a chemical box model. We confirm large scale model results that at temperatures T>210 K additional ozone loss -- mainly caused by hydrolysis of BrONO2 and N2O5 -- scales in proportion with the aviation-produced increase of the background aerosol surface area. However, at lower temperatures (< 210 K) we isolate two effects which efficiently reduce the aircraft-induced perturbation: (1) background particles growth due to H2O and HNO3 uptake enhance scavenging losses of aviation-produced liquid particles and (2) the Kelvin effect efficiently limits chlorine activation on the small aircraft-induced droplets by reducing the solubility of chemically reacting species. These two effects lead to a substantial reduction of heterogeneous chemistry on aircraft-induced volatile aerosols under cold conditions. In contrast we find contrail ice particles to be potentially important for heterogeneous chlorine activation and reductions in ozone levels. These features have not been taken into consideration in previous global studies of the atmospheric impact of aviation. Therefore, to parameterize them in global chemistry and transport models, we propose the following parameterisation: scale the hydrolysis reactions by the aircraft-induced surface area increase, and neglect heterogeneous chlorine reactions on liquid plume particles but not on ice contrails and aircraft induced ice clouds.