Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.
<p>In contrast to the German power supply, the energy supply in many West African countries is very unstable. Frequent power outages are not uncommon. Especially for critical infrastructures, such as hospitals, a stable power supply is vital. To compensate for the power outages, diesel generators are often used. In the future, these systems will increasingly be supplemented by PV systems and storage, so that the generator will have to be used less or not at all when needed. For the design and operation of such systems, it is necessary to better understand the atmospheric variability of PV power generation.&#160; For example, there are large variations between rainy and dry seasons, between days with high and low dust levels - caused by sandstorms (harmattan) or urban air pollution.</p><p>In our paper, we investigate different aspects of aerosol characteristics on PV hybrid systems in West Africa. Based on measured data from different sources (AERONET, DACCIWA, EnerSHelF), we will investigate the influence of aerosol density and type on PV performance by comparing a non-spectrally resolved (Neher et al., 2017) and a spectrally resolved PV performance model (Herman-Czezuch et al., submitted). Due to the materials used (semiconductors e.g. silicon, gallium arsenide, cadmium telluride), photovoltaic cells are spectrally selective. This means that only radiation of certain wavelengths is converted into electrical energy. A material property called spectral sensitivity characterizes a certain degree of solar radiation conversion into the electric current for each wavelength of sunlight. On the other hand, different types of aerosols can be distinguished by their scattering and absorption properties. A fundamental study of the impact of spectral effects due to different aerosol types is essential to improve PV power predictions under aerosol-dominated situations, such as dust storms or urban smog.</p><p>The current study is part of the EnerSHelF (Energy Self-sufficiency of Health Facilities in Ghana) research project funded by the German Federal Ministry of Education and Research (BMBF) and coordinated by the Bonn-Rhein-Sieg University of Applied Sciences (H-BRS)</p><p>Here we present model results in which we systematically investigate the impact of aerosols on PV performance for different PV technologies. In addition, we show results of a case study investigating the impact of desert dust on a real PV hybrid system during the harmattan season (Bebber et al., 2021).</p><p><strong>&#160;</strong><strong>References</strong></p><ul><li>Bebber, M., et al., &#8222;PV-Diesel-Hybrid-System f&#252;r ein Krankenhaus in Ghana - Anbindung eines PV-Batteriespeichermodells an ein bestehendes Generatormodell.&#8220; Hochschule Bonn-Rhein-Sieg, 2021 (IZNE Working Paper Series, Nr. 21/3.) (Research Paper.) ISBN 978-3-96043-091-9</li> <li>Herman-Czezuch, A., et al., &#8220;Impact of solar spectrum on the efficiency of photovol-taic cells &#8211; spectrally resolved PV performance model&#8221;, submitted to Solar Energy, 2021</li> <li>Neher, I, et al., &#8220;Impact of aerosols on photovoltaic energy production - Scenarios from the Sahel Zone&#8221;. In: Energy Procedia, Vol.125, 2017, S. 170-179</li> </ul>
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