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Energy and buildings 326, 114987 (2025). doi:10.1016/j.enbuild.2024.114987 Published by Elsevier Science, Amsterdam [u.a.]

The current body of research on the gender-energy nexus has largely concentrated on the effects of energy poverty within households, highlighting the impact on women in domestic settings. Nonetheless, women entrepreneurs engaged in productive activities are also pivotal in adopting new energy technologies. This second version of the dataset incorporates significant updates, presenting raw and processed data from 40 face-to-face interviews conducted across multiple African countries, now including Nigeria, which was previously excluded. The dataset focuses on micro and small-sized enterprises with at least one female owner, offering a more unified and comprehensive sample to assess energy access among women entrepreneurs in Africa and explore the potential for renewable energy adoption. The data collection through semi-structured, face-to-face interviews occurred between February and October 2024. The interviews followed a predetermined questionnaire designed to collect quantitative and qualitative data. A new notes section explains the main methods and references used in the dataset. Distinctions are also made between primary and secondary data for appliance power ratings, ensuring transparency in cases where secondary data supplements gaps. This version includes updated technical data, such as time-of-use information for appliances, enhancing the dataset's strength in providing technical insights. Key components of the dataset include: - Socio-economic factors: Enterprise location, ISIC division and industry sector classification, main production goods, gender-based ownership structures, enterprise formality (based on registration), year of establishment or business start, enterprise size (number of employees), profit margins, and business challenges related to the owner's gender. - Energy access characteristics: Type of energy carriers used, subapplications, energy supply shortages, energy consumption levels, type, number, power rating of appliances used, temperature requirements, time-of-use data, and energy expenditure. - Potential for renewable energy adoption: Type and amount of process waste, perceived barriers and drivers for renewable energy adoption, willingness to invest in new technologies, and preferred financing methods for such technologies. This updated dataset provides enhanced value for researchers, policymakers, and practitioners aiming to understand the energy access landscape for women entrepreneurs in Africa. It offers a robust foundation for developing targeted interventions that promote gender equity in energy access and encourage renewable energy adoption.

A nuclear reactor based on MIT BEAVRS benchmark was used as a typical power generating Pressurized Water Reactor (PWR). The PARCS v3.2 nodal-diffusion core simulator was used as a full-core reactor physics solver to emulate the operation of a reactor and to generate training, and validation data for the ANN. The ANN was implemented with dedicated Python 3.8 code with Google's TensorFlow 2.0 library. The effort was based to a large extent on the process of appropriate automatic transformation of data generated by PARCS simulator, which was later used in the process of the ANN development. Various methods that allow obtaining better accuracy of the ANN predicted results were studied, such as trying different ANN architectures to find the optimal number of neurons in the hidden layers of the network. Results were later compared with the architectures proposed in the literature. For the selected best architecture predictions were made for different core parameters and their dependence on core loading patterns. In this study, a special focus was put on the prediction of the fuel cycle length for a given core loading pattern, as it can be considered one of the targets for plant economic operation. For instance, the length of a single fuel cycle depending on the initial core loading pattern was predicted with very good accuracy (>99%). This work contributes to the exploration of the usefulness of neural networks in solving nuclear reactor design problems. Thanks to the application of ANN, designers can avoid using an excessive amount of core simulator runs and more rapidly explore the space of possible solutions before performing more detailed design considerations.