Abstract
In the upcoming years, European countries have to make a strong bet on solar energy. Small photovoltaic systems are able to provide energy for several applications like housing, traffic and street lighting, among others. This field is expected to have a big growth, thus taking advantage of the largest renewable energy source existing on the planet, the sun. This paper proposes a computational model able to simulate the behavior of a stand-alone photovoltaic system. The developed model allows to predict PV systems behavior, constituted by the panels, storage system, charge controller and inverter, having as input data the solar radiation and the temperature of the installation site. Several tests are presented that validates the reliability of the developed model.

Abstract
The use of mobile conversations is increasing all around the world. A conversational agent (CA) is mostly useful due to the fast response times and their simple nature. Recently, we have seen the development and increasing use of dialog systems on the Web. A conversational agent (CA) is a system capable of conversing with a user in natural language, in a way that it simulates a human dialog. Examples of CA can be found in several areas, including healthcare, entertainment, business, and education. In this paper a state of the art review of these dialog systems is presented, comprising different categories, different approaches and trends. The purpose of this work is to identify and compare the main existing approaches for building CA, categorizing them and highlighting the main strengths and weaknesses. Furthermore, it seeks to contextualize their use in an educational context and to discover the issues related to this task that may help in the choice of future investigations in the area of conversational natural language processing in educational context.

Abstract
Due to the rapid development of artificial intelligence, popular Virtual Assistants like Amazon Alexa or Google Assistant, can be applied to a wide variety of business areas. One area in which Virtual Assistants can be very useful is in Education, specially due to the pandemics that is occurring during the last years, as it can provide to students, teachers and staff an alternative administration tool as well as introduce new learning processes in classroom or on online classes. This work reviews and analyses some applications of Virtual Assistants in the education process. The reviewed work relies mainly on three categories: Student engagement with academic life, Education process during lessons and Learning of foreign languages. The presented solutions generally have great potential but the majority are simple proof of concepts and need more development and proper tests to enable retrieving more accurate results.

Abstract
[No abstract available]

Abstract
Electric vehicles (EVs) allow a significant reduction in harmful gas emissions, thus improving urban air quality. However, the widespread adoption of this technology is limited by several factors, resulting in heterogeneous deployment in urban areas. This raises challenges regarding the planning of public electric vehicle charging infrastructure (EVCI), requiring adaptive strategies to ensure comprehensive and efficient coverage. This study introduces an innovative method that leverages geographic information systems to pinpoint appropriate sizes and suitable locations for public EVCI within urban environments. Initially, a Bass diffusion model is employed to estimate EV adoption rates by regions, enabling the determination of the appropriate sizes of EVCI necessary for each of them. Subsequently, a multi-criteria decision-making approach is applied to identify the suitable locations for EV charger installation within each region. In this way, EVCI locations are selected using spatial criteria, which ensure they are near common areas of interest and easily accessible through the road network. To validate the effectiveness and applicability of the proposed method, tests using geospatial data from a city in Brazil were carried out. The findings suggest that EVCI planning without proper spatial analysis may result in inefficient locations and inadequate sizes, which may discourage potential EV adopters and hinder widespread adoption of this technology.

Abstract
The increasing integration of Artificial Intelligence (AI) into Building Energy Management Systems (BEMS) is revolutionizing energy optimization by enabling real-time monitoring, predictive analytics, and automated control. While these advancements improve energy efficiency and sustainability, the opacity of AI models poses challenges in interpretability, limiting user trust and hindering widespread adoption in operational decisionmaking. Ensuring transparency is crucial for aligning AI insights with building performance requirements and regulatory expectations. This paper presents EI-Build, a novel Explainable Artificial Intelligence (XAI) framework designed to enhance the interpretability of intelligent automated BEMS. EI-Build integrates multiple XAI techniques, including Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Anchors, Partial Dependence Plots, Feature Permutation Importance, and correlation-based statistical analysis, to provide comprehensive explanations of model behavior. By dynamically tailoring the format and depth of explanations, EI-Build ensures that insights remain accessible and actionable for different user profiles, from general occupants to energy specialists and machine learning experts. A case study on photovoltaic power generation forecasting applied to a real BEMS context evaluates EI-Build's capacity to deliver to deliver both global and local explanations, validate feature dependencies, and facilitate cross-comparison of interpretability techniques. The results highlight how EI-Build enhances user trust, facilitates informed decision-making, and improves model validation. By consolidating diverse XAI methods into a single automated framework, EI-Build represents a significant advancement in bridging the gap between complex AI energy models and real-world applications.