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Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classification. The research results show that the algorithm improves the accuracy of traffic sign recognition more comprehensively and effectively, and it can be effectively applied in unmanned driving technology, which will also bring new breakthroughs for the sustainable development of public transport.

DC microgrids, along with existing AC grids, are a future trend in energy distribution systems. At the same time, many related issues are still undefined and unsolved. In particular, uncertainty prevails in isolation requirements between AC grids and novel microgrids as well as in the grounding approaches. This paper presents a critical technical analysis and an overview of possible grounding approaches in DC systems and the feasibility of avoiding isolation between AC and DC grids.

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

Weather conditions have a significant impact on the installation of offshore wind turbines. The rules for installation set clear limits. These limits are usually based on estimations of various experts and not on real assumptions and measurements on-site. When wind speeds and wave heights are too high, work cannot be carried out, and this leads to delays and additional costs. Therefore, we have carried out a measurement campaign during the installation of rotor blades to investigate to which extent the limits can be adjusted by using a tuned mass damper. The results from the measurement campaign—specifically empirically derived significant wave height limits—are used in a discrete event simulation. This study simulates delays resulting from weather conditions. Based on this, the total installation costs are considered. The results of the measurement campaign show that a safe installation with the use of a damper is possible at wave heights of up to 1.6 m. With the discrete event simulation, it is possible to prove that 17.9% can be saved for the costs of the installation vessel. In addition, the wind farm could be erected 32 days faster. Thus, it can be stated that the use of a tuned mass damper simplifies the installation from a technical point of view and is economical.

Sub-Saharan Africa, especially its rural areas, faces significant challenges in achieving universal electrification despite its abundant renewable energy resources. The region has the highest population without access to electricity, largely due to economic, infrastructural, and geographical barriers. Energy poverty is a critical issue that hinders sustainable development and exacerbates inequalities. Namibia’s sustainable energy policy aligns with the global Sustainable Development Goals (SDGs), particularly SDG 7, which aims to provide affordable and reliable modern energy access for all. The policy emphasizes mini-grids and decentralized power systems as key strategies for rural electrification. However, despite increased deployment of mini-grids, these solutions often struggle with long-term sustainability. This research explores cost-effective electrification strategies through scenario-based modeling to reduce energy poverty and expand energy access in Namibia’s rural communities, focusing on the existing mini-grids in Tsumkwe and Gam. Using a comprehensive methodology that incorporates HOMER Pro for mini-grid capacity expansion and MS Excel for evaluating main-grid extensions, this study aims to identify the most feasible and economical electrification solutions. The analysis compares electricity supply, total net present cost, and the levelized cost of electricity across these systems. The findings will offer insights into addressing energy poverty in Namibia and provide recommendations for sustainable and scalable rural electrification across Sub-Saharan Africa.

This research aimed to evaluate the comparative biogas yields of waste (peels) of selected fibrous materials from the West African region: cassava, plantain, a mixture of cassava, plantain and yam. Three models: The Boyle model, the Modified Boyle’s model, and the Buswell and Müller’s model were used to determine the theoretical maximum biomethane potential (TBMP), while the Hohenheim Biogas Yield test (D-HBT) was used to undertake a batch test of anaerobic digestion. The samples were co-digested with digested sewage sludge (DSS) for 39 days, with an operating temperature of 37 ± 0.5 °C. The study draws comparisons between the TBMPs and the experimental results, the experimental results of the different substrates, and the experimental results and figures reported in the literature. From the experimental results, plantain peels had the highest biogas yield (468 ± 72 mL/g oTS), followed by a mixture of yam, cassava and plantain peels (362 ± 31 mL/g oTS) and cassava peels obtained the least biogas yield (218 ± 19 mL/g oTS). TBMPS of 204.04, 209.03 and 217.45 CH4 mL/g oTS were obtained for plantain peels, a mixture of yam, cassava and plantain peels and cassava peels, respectively, evaluated using Boyle’s model. For all the samples, the TBMPS (205.56, 209.03 and 218.45 CH4 mL/g oTS, respectively) obtained using the Buswell and Mueller model were slightly higher than those obtained by both the Boyle and the modified Boyle’s model (163.23, 167.22 and 174.76 CH4 mL/g oTS, respectively). While the study result is sufficient to imply that generating biogas from fibrous waste materials in its mixture form is a valuable approach, it is not sufficient to conclude that the use of these waste materials in its naturally occurring mixture form has a technical added advantage in co-digestion over their individual potential. However, future studies could explore this possibility with different fractions of the mixture with a view to optimising generation. The study finds that theoretically modelling the biogas potential of fibrous materials is a good method for biogas evaluation despite having overestimation tendencies, as this challenge could be corrected by applying factors that result in these tendencies, biodegradability indices. The data can, therefore, find use in fibrous waste treatment and waste-to-energy technologies, especially in Africa. This application will not be negatively affected by whether single waste streams are used or their mixture.

Green hydrogen is a promising energy carrier for the decarbonization of hard-to-abate sectors and supporting renewable energy integration, aligning with carbon neutrality goals like the European Green Deal. Iceland’s abundant renewable energy and decarbonized electricity system position it as a strong candidate for green hydrogen production. Despite early initiatives, its hydrogen economy has yet to significantly expand. This study evaluated Iceland’s hydrogen development through stakeholder interviews and a techno-economic analysis of alkaline and PEM electrolyzers. Stakeholders were driven by decarbonization goals, economic opportunities, and energy security but faced technological, economic, and governance challenges. Recommendations include building stakeholder confidence, financial incentives, and creating hydrogen-based chemicals to boost demand. Currently, alkaline electrolyzers are more cost-effective (EUR 1.5–2.8/kg) than PEMs (EUR 2.1–3.6/kg), though the future costs for both could drop below EUR 1.5/kg. Iceland’s low electricity costs and high electrolyzer capacity provide a competitive edge. However, this advantage may shrink as solar and wind costs decline globally, particularly in regions like Australia. This work’s findings emphasize the need for strategic planning to sustain competitiveness and offer transferable insights for other regions introducing hydrogen into ecosystems lacking infrastructure.