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The temperature of photovoltaic modules is modelled as a dynamic function of ambient temperature, shortwave and longwave irradiance and wind speed, in order to allow for a more accurate characterisation of their efficiency. A simple dynamic thermal model is developed by extending an existing parametric steady-state model using an exponential smoothing kernel to include the effect of the heat capacity of the system. The four parameters of the model are fitted to measured data from three photovoltaic systems in the Allgäu region in Germany using non-linear optimisation. The dynamic model reduces the root-mean-square error between measured and modelled module temperature to 1.58 K on average, compared to 3.03 K for the steady-state model, whereas the maximum instantaneous error is reduced from 20.02 to 6.58 K.
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 cooperation between researchers and practitioners during the different stages of the research process is promoted as it can be of benefit to both society and research supporting processes of ‘transformation’. While acknowledging the important potential of research–practice–collaborations (RPCs), this paper reflects on RPCs from a political-economic perspective to also address potential unintended adverse effects on knowledge generation due to divergent interests, incomplete information or the unequal distribution of resources. Asymmetries between actors may induce distorted and biased knowledge and even help produce or exacerbate existing inequalities. Potential merits and limitations of RPCs, therefore, need to be gauged. Taking RPCs seriously requires paying attention to these possible tensions—both in general and with respect to international development research, in particular: On the one hand, there are attempts to contribute to societal change and ethical concerns of equity at the heart of international development research, and on the other hand, there is the relative risk of encountering asymmetries more likely.
I. Einleitung II. Soziale Sicherung als Bestandteil entwicklungspolitischer Agenden – Eine internationale Perspektive III. Internationale Politikdiffusion und nationaler Politikwandel – Konzeptionelle Grundlagen IV. Die Rolle internationaler Politikdiffusion für den Wandel sozialer Sicherungssysteme – Empirische Evidenz V. Schlussfolgerungen
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.
This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
Green infrastructure improves environmental health in cities, benefits human health, and provides habitat for wildlife. Increasing urbanization has demanded the expansion of urban areas and transformation of existing cities. The adoption of compact design in urban planning is a recommended strategy to minimize environmental impacts; however, it may undermine green infrastructure networks within cities as it sets a battleground for urban space. Under this scenario, multifunctionality of green spaces is highly desirable but reconciling human needs and biodiversity conservation in a limited space is still a challenge. Through a systematic review, we first compiled urban green space's characteristics that affect mental health and urban wildlife support, and then identified potential synergies and trade-offs between these dimensions. A framework based on the One Health approach is proposed, synthesizing the interlinkages between green space quality, mental health, and wildlife support; providing a new holistic perspective on the topic. Looking at the human-wildlife-environment relationships simultaneously may contribute to practical guidance on more effective green space design and management that benefit all dimensions.
Green infrastructure has been widely recognized for the benefits to human health and biodiversity conservation. However, knowledge of the qualities and requirements of such spaces and structures for the effective delivery of the range of ecosystem services expected is still limited, as well as the identification of trade-offs between services. In this study, we apply the One Health approach in the context of green spaces to investigate how urban park characteristics affect human mental health and wildlife support outcomes and identify synergies and trade-offs between these dimensions. Here we show that perceived restorativeness of park users varies significantly across sites and is mainly affected by safety and naturalness perceptions. In turn, these perceptions are driven by objective indicators of quality, such as maintenance of facilities and vegetation structure, and subjective estimations of biodiversity levels. The presence of water bodies benefited both mental health and wildlife. However, high tree canopy coverage provided greater restoration potential whereas a certain level of habitat heterogeneity was important to support a wider range of bird species requirements. To reconcile human and wildlife needs in green spaces, cities should strategically implement a heterogeneous green infrastructure network that considers trade-offs and maximizes synergies between these dimensions.
Argentina substantially contributes to the global organic agriculture and food sector due to its large areas of organically managed agricultural land. However, most of the organic production is intended for export. Overall, food supply for the domestic organic market is hardly tapped. Based on this, we investigate the current importance of organic agriculture and food production as well as consumption attitudes and behavior within the country. The novelty of the study also lies in the observation, documentation and analysis of latest stakeholder‐driven developments towards organic agriculture and food. Furthermore, the publication allows the Argentinean organic market to be significantly more visible for the international audience.
New approaches in securing more sustainable urban food futures: case from Cologne-Bonn region
(2018)
Agricultural activities within city boundaries have a long history in both developed and developing countries. In this paper, a broad approach to Urban AgriCulture (UAC) is used, one that includes the production of crops in urban and peri-urban areas and ranges in developed countries from allotment gardens over community gardens to semi-entrepreneurial self-harvest farms and fully commercialized agriculture. With an empirical case study on UAC Initiatives in the Bonn/Rhein-Sieg region this work fills a gap since the lack of comprehensive and comparative studies on urban agriculture (UA) currently makes it difficult for researchers to identify the benefits of UA activities.
In January 2015, German retail and industry jointly started a sector-wide initiative ("Initiative Tierwohl" - ITW) to improve animal welfare standards. The principle of the ITW is communicated mostly via the websites of ITW and its participating companies. However, uncertainty remained whether or not these websites provide the necessary information consumers need on the ITW products. Based on Schwartz's basic human values, different types of consumers were identified by a cluster analysis (ward-method, k-means). The results showed that depending on expressed meta‐values (Self-Transcendence/Openness to Change Self-Enhancement or Conservation), respondents had different specific information sources and needs. Online sources were rarely mentioned, the majority of consumers referred to brochures, flyers and interpersonal contacts.
Contract-based nature protection schemes are a voluntary mechanism, with a limited contract duration, that aim to raise the acceptance of biodiversity conservation practices in agriculture among farmers and other land users. The purpose of this paper is to analyse the institutional settings of contract-based nature protection based on the– “Institutions of Sustainability” (IoS) framework in the German Rhine-Sieg district, and to outline the way in which policy measures should be designed to encourage farmers to participate in contract-based nature protection programmes. This was achieved by answering research questions to identify the challenges, potentials and obstacles of a contract-based nature protection scheme in different “sub-arenas” as defined in the IoS framework. Qualitative research methods were used as the methodology. The analysis shows that main constraints for sufficient implementation of contract-based nature protection schemes are the limited consideration of the impact of climate change during the contract period, the limited consideration of regional conditions as regards the measures taken on the ground and an inflexible contract duration.
Handlungsspielräume zur Gestaltung nachhaltiger Mobilität werden unzulänglich genutzt. Wissenschaftliche Erkenntnisse aus Gesundheit, Umwelt und Verkehr finden zögerlich Eingang in Verkehrspolitik. Konkurrenz der Ministerien für Wirtschaft, Verkehr, Umwelt und Gesundheit hindert an der Wahrnehmung der Wertschöpfung nachhaltiger Mobilität. Bemühungen um eine Entlastung der Innenstädte sind von effizienter Prävention weit entfernt, externe Kosten werden ignoriert. Biokraftstoffpflanzungen (mit Raubbau an Regenwäldern) erhöhen die Emissionen der industrialisierten Landwirtschaft und ruinieren Wasserressourcen. Diese Verschiebung in andere klimagasrelevante Produktionsbereiche verschlimmert die globalen CO2-Bilanzen. Wenige Einzelfälle sind verkehrs-ökologisch am Verursacherprinzip orientiert (z. B. in Österreich). Die WHO stellt Wissen bereit, unerwünschte Effekte vermeidbar zu machen. Über vermeidbare Gesundheitsfolgekosten (Atemwegserkrankungen, Übergewicht) liegt zwar genügend Material vor. Transdisziplinäre Betrachtungen der Wertschöpfung werden aber als nicht umsetzbar abqualifiziert.