This paper investigates the impact of dust accumulation and temperature on the performance of monocrystalline and polycrystalline photovoltaic (PV) panels under the climatic conditions of Niamey, Niger, with a focus on seasonal variations. High-resolution performance data were collected across the rainy, Harmattan, and hot seasons to evaluate power output losses. During the rainy season, natural cleaning by rainfall limited power and energy losses to 0.33% and 1.55% for monocrystalline, 5.98% and 6.67% for polycrystalline panels, respectively. In contrast, the Harmattan season showed the most severe reductions, with power and energy losses reaching, respectively, 66.46% and 65.55% for monocrystalline, 57.96% and 57.50% for polycrystalline, due to intense dust accumulation and atmospheric scattering. During the hot season, elevated temperatures and soiling combined to reduce performance by 54.6% and 52.31% for monocrystalline, 43.22% and 42.61% for polycrystalline, respectively. While monocrystalline panels perform better under clean conditions, they are more susceptible to environmental stressors than polycrystalline panels, which demonstrate greater resilience to dust and temperature effects. These findings highlight the importance of regular cleaning, antisoiling technologies, and climate-adaptive PV system design to sustain performance in dusty and high-temperature environments, such as the Sahel.

As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art approaches increasingly apply deep learning to sky camera and geostationary satellite observations, but fragmented datasets and inconsistent evaluation make it difficult to determine whether reported improvements generalize across climates, cloud regimes, and photovoltaic (PV) systems. Here we introduce SolarBench, an open global benchmark for image-based solar nowcasting. SolarBench harmonizes more than six million sky and satellite images from 11 diverse sites spanning a decade, together with irradiance or PV output and auxiliary atmospheric data. An accompanying toolbox supports reproducible data access, processing, model development, and evaluation. Using SolarBench, we benchmark representative models and reveal a gap between average forecasting accuracy and the ability to capture rapid solar fluctuations. We further quantify predictability across cloud regimes and demonstrate data-efficient adaptation to new PV systems. SolarBench provides an extensible foundation for fair comparison and methodological innovation in solar nowcasting.

In Ghana, unreliable public grid infrastructure greatly impacts rural healthcare, where diesel generators are commonly used despite their high financial and environmental costs. Photovoltaic (PV)-hybrid systems offer a sustainable alternative, but require robust, predictive control strategies to ensure reliability. This study proposes a sector-specific Model Predictive Control (MPC) approach, integrating advanced load and meteorological forecasting for optimal energy dispatch. The methodology includes a long-short-term memory (LSTM)-based load forecasting model with probabilistic Monte Carlo dropout, a customized Numerical Weather Prediction (NWP) model based on the Weather Research and Forecasting (WRF) framework, and deep learning-based All-Sky Imager (ASI) nowcasting to improve short-term solar predictions. By combining these forecasting methods into a seamless prediction framework, the proposed MPC optimizes system performance while reducing reliance on fossil fuels. This study benchmarks the MPC against a traditional rule-based dispatch system, using data collected from a rural health facility in Kologo, Ghana. Results demonstrate that predictive control greatly reduces both economic and ecological costs. Compared to rule-based dispatch, diesel generator operation and fuel consumption are reduced by up to 61.62% and 47.17%, leading to economical and ecological cost savings of up to 20.7% and 31.78%. Additionally, system reliability improves, with battery depletion events during blackouts decreasing by up to 99.42%, while wear and tear on the diesel generator and battery are reduced by up to 54.93% and 37.34%, respectively. Furthermore, hyperparameter tuning enhances MPC performance, introducing further optimization potential. These findings highlight the effectiveness of predictive control in improving energy resilience for critical healthcare applications in rural settings.

Although rooftop grid-connected solar PV systems (RGCSPVS) play a crucial role in the global energy transition to mitigate climate change, reactive power imbalance remains a significant power quality challenge, intensifying as the system approaches its optimal penetration limit. This study examines the amount of reactive power an optimally sized GCSPVS can accommodate without exceeding voltage limits. A Python-based dynamic programming approach placed optimally, shunt capacitor (SC) targeting high loads, voltage drops, and losses nodes. The Conditional New Adaptive Foraging Tree Squirrel Search Algorithm (CNAFTSSA) otherwise applied to determine the most effective SC injection locations. In the high-load scenario, 46.20 kVAR of SC injection reduced system losses from 72.36% to 82.87%, improving the power factor from 0.51 to 0.69. The high-voltage-drop scenario introduced 10.27 kVAR, reducing losses from 71.57% to 73.03% and enhancing the power factor from 0.53 to 0.55. For high-loss nodes, 66.73 kVAR reduced losses from 68.21% to 82.27% and improved the power factor from 0.31 to 0.68. The CNAFTSSA scenario injected 65.70 kVAR, reducing losses from 68.34% to 82.88% and improving the power factor from 0.31 to 0.67. These results highlight SCs’ role in minimizing losses and enhancing power quality, with the CNAFTSSA approach demonstrating superior performance.© 2017 Elsevier Inc. All rights reserved.

The radiative effect resulting from the interaction of aerosol with atmospheric radiation is important for our understanding of the climate system. In an extension of Witthuhn et al. [Atmos. Chem. Phys., 21, 14591–14630 (2021)], the surface irradiance and aerosol radiative effect under clear sky conditions are investigated for the region of Germany and the period from 2003 to 2020. This study is based on observations of solar irradiance at 25 stations throughout Germany by the observational network of the German Weather Service, as well as explicit radiative transfer simulations using the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis as input. It is found that aerosol optical depth decreases over Germany, as a consequence of a long-term change in aerosol composition. The aerosol clear sky radiative effect at the surface decreases in magnitude by 2.5Wm−2 10yr−1. A similar trend is observed in the surface irradiance in clear sky conditions, which increases by 3 Wm−2 10yr−1 in its daily mean.

A building’s energy storage demand depends on a variety of factors related to the specific local conditions such as building type, self-sufficiency-rate, and grid connection. Here, a newly developed bottom-up procedure is presented for classifying buildings in an urban building portfolio according to specific criteria. The algorithm uses publicly available building data such as building use, ground floor area, roof ridge height, solar roof potential, and population statistics. In addition, it considers the local gas grid (GG) as well as the district heating (DH) network. The building classification is developed for identifying typical building situations that can be used to estimate the demand for residential energy storage capacity. The developed algorithm is used to identify potential implementation of private photovoltaic(PV)-metal-hydride-storage (MHS) systems, for three scenarios, into the urban infrastructure for the city of Cologne. As result the statistical confidence interval of all analyzed buildings regarding their classification as well as corresponding maps is shown. Since similar data sets as used are available for many German or European metropolitan areas, the method developed with the assumptions presented in this work, can be used for classification of other urban and semi-urban areas including the assessment of their grid infrastructure.

This study examines the impact of integrating solar photovoltaic (PV) systems on power factor (PF) within low-voltage radial distribution networks, using empirical data from the Energy Self-Sufficiency for Health Facilities in Ghana (EnerSHelF) project sites in Ghana. The research included simulations focusing on optimal PV integration, with and without PF considerations, and the strategic placement of PV and shunt capacitors (SC). Three scenarios evaluated PV injection at high-load demand nodes, achieving penetration levels of 85.00 percent, 82.88 percent with high voltage drop, and 100.00 percent with high loss nodes. Additionally, three scenarios assessed SC allocation methods: proportional to the node's reactive power demand (Scenario I), even distribution (Scenario II), and proportional to installed PV capacity at PV nodes (Scenario III).The analysis used a twin-objective index (TOI), combining voltage deviations and power factor degradation. Results showed significant PV curtailment was necessary to achieve standard PF. Optimal penetration levels, considering TOI, reduced PV penetration from 85.00 percent to 63.75 percent, 82.88 percent to 57.38 percent, and 100.00 percent to 72.50 percent for high load, high voltage drops, and high loss nodes, respectively. Notably, all scenarios showed a concerning PF of 0.00 at dead-end nodes (P20, P21, P22).Scenario I achieved PF ranges of -0.26 to 1.00 with PV at high load, -0.69 to 1.00 with PV at high voltage drop, and 0.95 to 1.00 with PV at high loss nodes. Scenario II produced similar ranges, -0.48 to 1.00, -1.00 to 0.99, and 0.30 to 0.96, with PV placement at high load, voltage drops, and loss nodes, respectively. Scenario III yielded ranges of -0.19 to 0.97 (high load), -0.23 to 1.00 (high voltage drop), and 0.86 to 0.96 (high losses).The study concluded that the most effective strategy involves installing PVs at high-loss nodes and distributing SCs proportionally to the node's reactive power demand (Scenario I). This approach achieved a more uniform PF pattern throughout the network, highlighting the practical implications of strategic PV placement and targeted reactive power compensation for maintaining a healthy and efficient distribution system with solar PV integration.

Energy meteorology is an applied research field of meteorology that focuses on the study and prediction of weather conditions and events that affect energy production and use. This field has become increasingly important as the energy industry has become more dependent on weather conditions, especially in the areas of renewable energy sources such as wind energy, solar energy, and hydropower. The following paper has been written by experts of the Committee on Energy Meteorology of the German Meteorological Society summarizing their more than 30 years of experience and lessons learnt. It gives an overview of activities in energy meteorology that are already essential for the transformation of energy systems to systems with high shares of renewable energies. Building on this, the experts have created a vision of future topics that describe the future research landscape of energy meteorology. The authors explain that work in energy meteorology in recent years has primarily been concerned with the physically based modeling of wind and solar power generation and the development of short-term forecasting systems. In future years, a significant expansion of work in the areas of energy system modeling, digitalization, and climate change is expected. This includes the detailed consideration of regionally specified spatiotemporal variability for system design, the integration of artificial intelligence skills, the development of weather-related consumption based on smart meters, and the mapping of the effects of climate change on the energy system in planning and operating processes. VC2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND) license (https://creativecommons.org/licenses/by-nc-nd/4.0/).https://doi.org/10.1063/5.0231754

The shift towards renewable energy sources has heightened the interest in solar photovoltaic (SPV) systems, particularly in grid-connected configurations, to enhance energy security and reduce carbon emissions. Grid-tied SPVs face power quality challenges when specific grid codes are compromised. This study investigates and upgrades an integrated 90 kWp solar plant within a distribution network, leveraging data from Ghana's Energy Self-Sufficiency for Health Facilities (EnerSHelF) project. The research explores four scenarios for SPV placement optimization using dynamic programming and the Conditional New Adaptive Foraging Tree Squirrel Search Algorithm (CNAFTSSA). A Python-based simulation identifies three scenarios, high load nodes, voltage drop nodes, and system loss nodes, as the points for placing PV for better performance. The analysis revealed 85%, 82.88%, and 100% optimal SPV penetration levels for placing the SPV at high load, voltage drop, and loss nodes. System active power losses were reduced by 72.97%, 71.52%, and 70.15%, and reactive power losses by 73.12%, 71.86%, and 68.11%, respectively, by placing the SPV at the above three categories of nodes. The fourth scenario applies to CNAFTSSA, achieving 100% SPV penetration and reducing active and reactive power losses by 72.33% and 72.55%, respectively. This approach optimizes the voltage regulation (VR) from 24.92% to 4.16%, outperforming the VR of PV placement at high load nodes, voltage drop nodes, and loss nodes, where the voltage regulations are 5.25%, 9.36%, and 9.64%, respectively. The novel CNAFTSSA for optimal SPV placement demonstrates its effectiveness in achieving higher penetration levels and improving system losses and VR. The findings highlight the effectiveness of strategic SPV placement and offer a comprehensive methodology that can be adapted for similar power distribution systems.

Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRFSolar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model's capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region.

The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m-2 (19%); MAE = 172 W m-2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.

Estimates of global horizontal irradiance (GHI) from reanalysis and satellite-based data are the most important information for the design and monitoring of PV systems in Africa, but their quality is unknown due to the lack of in situ measurements. In this study, we evaluate the performance of hourly GHI from state-of-the-art reanalysis and satellite-based products (ERA5, MERRA-2, CAMS, and SARAH-2) with 37 quality-controlled in situ measurements from novel meteorological networks established in Burkina Faso and Ghana under different weather conditions for the year 2020. The effects of clouds and aerosols are also considered in the analysis by using common performance measures for the main quality attributes and a new overall performance value for the joint assessment. The results show that satellite data performs better than reanalysis data under different atmospheric conditions. Nevertheless, both data sources exhibit significant bias of more than 150 W/m2 in terms of RMSE under cloudy skies compared to clear skies. The new measure of overall performance clearly shows that the hourly GHI derived from CAMS and SARAH-2 could serve as viable alternative data for assessing solar energy in the different climatic zones of West Africa.

Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m−2 (7.35 W m−2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m−2 under clear and cloudy skies, respectively. During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.

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.ReferencesFabel, Y., Nouri, B., Wilbert, S., Blum, N., Triebel, R., Hasenbalg, M., Kuhn, P., Zarzalejo, L. F., and Pitz-Paal, R.: Applying self-supervised learning for semantic cloud segmentation of all-sky images, Atmospheric Measurement Techniques, 15, 797–809, https://doi.org/10.5194/amt-2021-1, 2022.Nouri, B., Blum, N., Wilbert, S., and Zarzalejo, L. F.: A Hybrid Solar Irradiance Nowcasting Approach: Combining All Sky Imager Systems and Persistence Irradiance Models for Increased Accuracy, Solar RRL, 2100442, https://doi.org/10.1002/solr.202100442, 2021.Paletta, Q., Arbod, G., and Lasenby, J.: Benchmarking of deep learning irradiance forecasting models from sky images – An in-depth analysis, Solar Energy, 224, 855–867, https://doi.org/10.1016/j.solener.2021.05.056, 2021.Pedro, H. T. C., Larson, D. P., and Coimbra, C. F. M.: A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods, Journal of Renewable and Sustainable Energy, 11, 36102, https://doi.org/10.1063/1.5094494, 2019.Yang, H., Wang, L., Huang, C., and Luo, X.: 3D-CNN-Based Sky Image Feature Extraction for Short-Term Global Horizontal Irradiance Forecasting, Water, 13, 1773, https://doi.org/10.3390/w13131773, 2021.

West Africa has a great potential for the application of solar energy systems, as it combines high levels of solar irradiance with a lack of energy production. Southern West Africa is a region with a very high aerosol load. Urbanization, uncontrolled fires, traffic as well as power plants and oil rigs lead to increasing anthropogenic emissions. The naturally circulating north winds bring mineral dust from the Sahel and Sahara and monsoons - sea salt and other oceanic compounds from the south. The EU-funded Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) project (2014–2018), dlivered the most complete dataset of the atmosphere over the region to date. In our study, we use in-situ measured optical properties of aerosols from the airborne campaign over the Gulf of Guinea and inland, and from ground measurements in coastal cities. Based on an analysis of the aerosol optical properties form the DACCIWA measurement campaign, the impact of aerosol on PV power is investigated for polycrystalline silicon and amorphous silicon technology using a spectrally resolved model chain. The model considers both spectral effects on global irradiance due to different aerosol properties as well as the spectral response of different PV technologies. First, the contribution of various aerosol types (mineral dust, biomass burning and anthropogenic pollution) derived from a post-project classification is studied. Subsequently, differences between these imaginary aerosol scenarios and a real case during a biomass burning outbreak on July 13, 2016 in Benin are presented. The results show that aerosol emissions due to the biomass outbreak on the day of the case study in Cotonou lead to solar flux losses of up to 55% and photovoltaic power reduction of up to 81% for the polycrystalline cell and 78% for the amorphous cell. The relative impact of aerosols differs depending on aerosol type and concentration, being larger for low solar zenith angles than at noon. For the situation studied in Cotonou, Benin, we are able to show that the inclusion of spectral aspects leads to a significant effect when calculating the PV power. Comparing the effects of aerosols on the photovoltaic power of the two technologies, we find that the amorphous cell suffers a greater reduction in power during the morning and evening hours - when there is more diffuse irradiance - of 36% than the polycrystalline cell (27%). Conversely, in the middle of the day, we observe greater PV power reduction of the polycrystalline cell of 12% compared to the amorphous cell (8%). Acknowledgements: Funding was provided by the German BMWi under contract 0350009A and BMBF under contract 03SF0567A-.

Proposal of a poster for the EMS2022Intention: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 aim of the project is therefore to develop PV-based energy solutions for healthcare facilities and to improve the reliability and integrability of such systems in the local electricity grid.The work is based on a measurement campaign that has been running since 2020 at three hospitals spread across the country. The variables measured include:Global tilted irradiance (GTI)Soiling ratio and temperature of the PV panelsAll-sky camera recordingsLoad measurement aggregate (grid node)Load measurement sub-distribution (departments and devices)In addition, weather stations are operated at the sites to improve weather forecasts.These datasets can be used to follow different approaches to managing the harsh conditions caused by dry and rainy seasons, and to design and control PV hybrid systems appropriately.According to the World Bank (2017) only 3% of the population in West Africa and the Sahel can currently access PV power through off-grid systems. As an important catalyst for sustainable development, access to a reliable source of clean energy is vital for inclusive economic development, improved human health, wellbeing and security. As such, EnerSHelF can contribute to Sustainable Development Goals (SDG) of health (SDG 3), energy (SDG 7) and partnerships (SDG 17).

West Africa has great potential for the use of solar energy systems, as it has both a high solar radiation rate and a lack of energy production. West Africa is a very aerosol-rich region, whose effects on photovoltaic (PV) use are due to both atmospheric conditions and existing solar technology. This study reports the variability of aerosol optical properties in the city of Koforidua, Ghana over the period 2016 to 2020, and their impact on the radiation intensity and efficiency of a PV cell. The study used AERONET ground (Giles et al., 2019) and satellite data produced by CAMS (Gschwind, et al., 2019), which both provide aerosol optical depth (AOD) and metrological parameters used for radiative transfer calculations with libRadtran (Emde, et al., 2016). A spectrally resolved PV model (Herman-Czezuch et al., 2022) is then used to calculate the PV yield of two PV technologies: polycrystalline and amorphous silicon. It is observed that for both data sets, the aerosol is mainly composed of dust and organic matter, with a very increased AOD load during the harmattan period (December-February), also due to the fires observed during this period. We compared CAMS satellite data with AERONET ground data. A good annual correlation (correlation coefficient, R2 ~0.8) was observed between the CAMS and AERONET AOD data. However, CAMS satellites tend to underestimate the high AOD measured on the ground by AERONET photometers; but they also overestimate the low AOD compared to AERONET. Both datasets also show differences in the average assumed optical properties of the aerosol encountered. A detailed analysis of the year 2020 shows a daily reduction in PV yield for the polycrystalline cell of up to 90% with the AERONET data and of up to 32% with the CAMS data. For the amorphous cell, the daily reduction in PV yield is up to 71% with the AERONET data and 34% with the CAMS data. These strong differences are due to the seasonal dispersion of the measured AODs but also to the variability of water vapor and ozone concentrations provided by CAMS and AERONET over Koforidua. Acknowledgements: Funding was provided by the German BMBF under contract 03SF0567A-. References * Emde et al. (2016). The libRadtran software package for radiative transfer calculations (version 2.0.1). Geosci. Model Dev. 9 (5), 1647-1672. * Giles et al. (2019). Advancements in the Aerosol Robotic Network (AERONET) Version 3 database. Atmospheric Measurement Technique, 12(1), 169-209. * Gschwind et al. (2019). Improving the McClear model estimating the downwelling solar radiation at ground level in cloud free conditions – McClear-V3. Meteorol. Z./Contrib. Atm. Sci. 28, 147-163. * Herman-Czezuch et al. (2022). Impact of Aerosols on Photovoltaic Energy Production Using a Spectrally Resolved Model Chain: Case Study of Southern West Africa. submitted to Solar Energy

In her recent article, Bender discusses several aspects of research-practice-collaborations (RPCs). In this commentary, we apply Bender's arguments to experiences in engineering research and development (R&D). We investigate the influence of interaction with practice partners on relevance, credibility, and legitimacy in the special engineering field of product development and analyze which methodological approaches are already being pursued for dealing with diverging interests and asymmetries and which steps will be necessary to include interests of civil society beyond traditional customer relations.