Ing. Marek Miltner

The global shift to electric vehicles (EVs) necessitates strategic deployment of public charging infrastructure to meet growing demand. Our study focuses on Prague, Czechia, analyzing real-world data to understand EV charging demand. Utilizing charging session and geospatial data, we categorize charging points by urban context and assess factors influencing demand. Geospatial analysis helps identify potential charging sites based on accessibility, location, and population density. Insights from temporal demand variations, share of charging instances per area type, and load profile characteristics guide infrastructure planning. Despite limitations in data scope and geographical specificity, this study offers valuable insights into public charging behavior, laying groundwork for future enhancements and predictive modeling to inform efficient charger placement in urban EV infrastructure.

The paper discusses the advancement of distributed renewable energy systems through the implementation of detailed net load forecasting. It emphasizes the use of artificial intelligence, machine learning, and data science to predict energy demands and optimize the integration of renewable sources into the power grid. The study highlights the significance of precise forecasting in enhancing the efficiency and reliability of energy distribution, contributing to a sustainable energy future.

This contribution outlines the context of why Energy is the key to mitigating climate change related challenges, and the role of artificial intelligence in the power engineering network nexus. We propose specific methodologies and connect them to use cases that allow for the transition to more environmentally, and operationally sustainable power grids.

Just like in other parts of the world, the transition towards electric vehicles (EVs) in Europe, driven by the European Union's carbon reduction targets, underscores the critical need for strategic planning of public charging infrastructure expansion. However, while adoption of EVs is growing exponentially, buildup of charging points is slowing down as most favourable locations get depleted. In our contribution we propose a method for optimizing the development of charging infrastructure by integrating a machine learning based model incorporating observed charging behavior and electromobility growth scenarios. Based on real-world data collected from public charging stations, the research estimates EV charging demand under various scenarios for 2030 and 2050. Additionally, geospatial analysis categorizes potential charging station locations into residential, commercial, and leisure areas to guide strategic deployment. The study employs a three-step machine learning approach, encompassing data analysis, generative modelling, and demand projection, to provide insights into future charging infrastructure needs. Results suggest that accurate demand forecasting can facilitate efficient allocation of resources and support the decarbonization of the transportation sector. Furthermore, the study's methodology and findings can be replicated in other cities, offering a valuable tool for stakeholders and infrastructure developers in planning the strategic deployment of charging stations.

The impact of the European Union’s carbon footprint reduction targets and their effects on the transport sector can be seen in the gradual trend towards replacing the combustion engine vehicles fleet with plug-in hybrid or fully electric vehicles. With the rapid growth of electric vehicles, many questions and issues arise associated with EV charging. The key to sustaining this rapid fleet renewal is constructing a dense public charging station network. This research aims to assist stakeholders and EV charging infrastructure developers in planning the strategic deployment of public charging stations in terms of number, charging power, and their placement in strategic locations in a city.

The decarbonisation of the economy and society as a whole is considered one of society's greatest challenges. It has been set as a goal by countries at UN summits such as the recent COP26, the groundbreaking COP21 in Paris in 2015, or in the European Commission's vision of a "Green Deal for Europe" since the current EU leadership . Given the energy sector's contribution to the carbon footprint and its key role in decarbonisation of other parts of the economy, such as transport, it is not surprising that decarbonization has become one of the main contemporary issues for the energy industry. Within current knowledge, countless technological solutions are already being developed involving the involvement of renewable energy sources, more efficient energy supply management, and other innovations in various sectors.This paper aims to offer a general overview of how organisations self-report their impact on the climate and measures taken to limit it.

Organizations in both academia and industry have struggled with incorporating radical innovation structures within their established frameworks. This paper analyses the practical applications of recently proposed methods of organizational design in highly sophisticated engineering organizations, particularly when introducing a new, disruptive project that requires a different mentality than that of the parent organization. Recent literature has proposed an approach called the ambidextrous organization for these use cases. Therefore, in order to effectively gauge its practical applications, this approach is tested on an empirical case study of an advanced research team developing electric racing vehicles for academic purposes when it decides to expand into autonomous vehicle development.

Climate change is often described as one of the most pressing issues of our time, and thus it is not surprising that it has become an important topic in industry as well. While the declared push to reduce the climate impact of organisations is clear, the actual impact and the effectiveness of mitigation efforts is not always monitored. This paper aims to offer a general overview of how organisations self-report their impact on the climate and measures taken to limit it, across different industries and geographies. Based on observed differences, best practices are showcased based on successful case examples.

Combinatorial interaction testing (CIT) is a useful testing technique to address the interaction of input parameters in software systems. CIT has been used as a systematic technique to sample the enormous test possibilities. Most of the research activities focused on the generation of CIT test suites as a computationally complex problem. Less effort has been paid for the application of CIT. To apply CIT, practitioners must identify the input parameters for the Software-under-test (SUT), feed these parameters to the CIT test generation tool, and then run those tests on the application with some pass and fail criteria for verification. Using this approach, CIT is used as a black-box testing technique without knowing the effect of the internal code. Although useful, practically, not all the parameters having the same impact on the SUT. This paper introduces a different approach to use the CIT as a gray-box testing technique by considering the internal code structure of the SUT to know the impact of each input parameter and thus use this impact in the test generation stage. The case studies results showed that this approach would help to detect new faults as compared to the equal impact parameter approach.