Abstract
Artificial Intelligence (AI) is now ubiquitous in daily life, significantly impacting society by supporting decision-making. However, in many application areas, understanding the rationale behind AI decisions is crucial, highlighting the need for explainable AI (XAI). AI algorithms often lack transparency, making it hard to understand their inner workings. This work presents an overview of XAI solutions for decision support in mobility context. It addresses the complexity of explaining decision support models by offering explanations in various formats tailored to different user profiles. By integrating language models, XAI models may generate texts with varying technical detail levels, aiding ethical AI deployment and bridging the gap between complex models and human interpretability. This work explores the need for flexible explanation formats, supporting varied user profiles with graphical, textual, and tabular explanations. By integrating natural language processing models personalized explanations that are accurate, understandable, and accessible to a diverse audience can be generated. This study ultimately aims to support the task of making XAI robust and user-friendly, boosting its widespread use and application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The adoption of residential photovoltaic (PV) systems to mitigate the effects of climate change has been incentivized in recent years by government policies. Due to the impacts of these systems on the energy mix and the electrical grid, it is essential to understand how these technologies will expand in urban areas. To fulfill that need, this article presents an innovative method for modeling the diffusion of residential PV systems in urban environments that employs spatial analysis and urban characteristics to identify residences at the subarea level with the potential for installing PV systems, along with temporal analysis to project the adoption growth of these systems over time. This approach integrates urban characteristics such as population density, socioeconomic data, public environmental awareness, rooftop space availability, and population interest in new technologies. Results for the diffusion of PV systems in a Brazilian city are compared with real adoption data. The results are presented in thematic maps showing the spatiotemporal distribution of potential adopters of PV systems. This information is essential for creating efficient decarbonization plans because, while many households can afford these systems, interest in new technologies and knowledge of the benefits of clean energy are also necessary for their adoption.

Abstract
The number of electric vehicles has been increasing significantly due to various factors, such as the higher prices of fossil fuels, concerns about the increasing pollution, and the resulting incentive to use energy from renewable sources. There are currently a few charging facilities, which are still quite scattered, and several are still experimental, requiring appropriate planning of this infrastructure in order to support the growing number of electric vehicles adequately. Thus, optimising the location of charging stations becomes a critical issue, which can be achieved through the application of mathematical models and data analysis tools. An example is genetic algorithms, which have demonstrated their versatility in solving complex optimisation problems, especially those involving multiple variables. This work presents a proposal for a more comprehensive genetic algorithm model that encompasses all variables from the perspectives of all entities involved. Its experimentation was conducted using real data, with the aim of finding the best combination of locations, minimising the total number of stations and maximising the coverage of the area under study. Thus, it is essential to carefully consider user preferences, accessibility, energy demand, and existing electrical infrastructure to ensure an effective and sustainable installation. The findings highlight the crucial role of these computing tools in addressing complex problems from various viewpoints, leading to solutions that cater to the needs of all parties involved. While not necessarily perfect, these solutions represent a balanced compromise across multiple dimensions of the problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The effectiveness of optimizing complex problems is closely linked to the configuration of parameters in search algorithms, especially when considering metaheuristic optimization models. Although various automated methods for algorithm configuration have been proposed to alleviate users from manually tuning parameters, there is still unexplored potential in dynamically adjusting certain algorithm parameters during execution, which can lead to enhanced performance. The main objective is to comparatively analyze the effectiveness of manual parameter tuning compared to a dynamic online configuration approach based on reinforcement learning. To this end, the State-Action-Reward-State-Action (SARSA) algorithm is adapted to adjust the parameters of a genetic algorithm, namely population size, crossover rate, mutation rate, and number of generations. Tests are conducted with these two methods on benchmark functions commonly used in the literature. Additionally, the proposed model has been evaluated in a practical problem of optimizing energy trading portfolios in the electricity market. Results indicate that the reinforcement learning-based algorithm tends to achieve seemingly better results than manual configuration, while maintaining very similar execution times. This result suggests that online parameter tuning approaches may be more effective and offer a viable alternative for optimization in metaheuristic algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Currently, energy consumption in residential buildings is increasingly high. To meet demand, renewable energies are increasingly being used to produce more energy in a sustainable way, which has led to an increase in the load on the distribution network. Thus, with the exponential growth of dependence on technologies, studies on consumption patterns are increasingly common in order to try to understand the needs of the population and, in this way, make a more rational and efficient use of energy. This article aims to find consumption patterns in residential devices, considering specific houses. This work proposes the use of the Apriori algorithm, which allows the creation of several association rules among devices. The results, considering several scenarios in a house with 9 appliances, show that, despite the Apriori algorithm's difficulty in finding associations in household appliances with little time of use, several interesting association rules can be identified, providing relevant insights for future consumption flexibility models applications.

Abstract
Explainable Artificial Intelligence (XAI) aims to enhance the interpretability of Artificial Intelligence (AI) systems for humans. The goal is to ensure that algorithmic decisions and underlying data are understandable to non-technical stakeholders. Advanced Machine Learning (ML) models, such as deep neural networks, enable AI systems to process vast data and extract intricate patterns, akin to the human brain, but this complicates XAI development. Complex ML models require substantial data for training, exacerbating the challenge. Consequently, this paper proposes a novel approach to improve XAI for complex ML models, particularly those with large data needs. Using K-Means clustering, the paper proposes to identify relevant data instances to create similarity clusters. This filtering process focuses XAI on essential information, even with complex models, reducing the data set to find patterns and explanations, so that, using the same approach, only the best explanations are filtered efficiently. The paper proposes to implement and test this model with a case study on ML for PV generation forecasting in buildings. Results show that the proposed approach is able to generate explanations that are very similar to those generated when using the entire available data, in only a portion of the execution time, leveraging from the identification of a small number of representative data points. This approach, therefore, enhances the efficiency of XAI by achieving promising results with a smaller dataset. It also facilitates the development of more understandable and fastly provided solutions, which is essential for real-world XAI users such as electric mobility users that need PV forecasting explanations as support for their vehicles charging management.