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
This paper presents an educational resource to support the teaching of Portuguese sign language. This educational resource emerges in response to the significant needs for the development of adequate digital tools to support deaf people in different tasks, especially in the language learning process. This work is motivated by the results and conclusions from previous studies that identify augmented reality as one of the promising solutions to improve the learning and teaching processes, and benefits from the advances already accomplished in the development and application of augmented reality solutions in several domains of the educational environment. The educational resource presented in this work is an augmented reality solution that enables associating hand gestures, representative of Portuguese sign language, to different cards, which represent different letters of the alphabet. In this way, it is possible to associate the alphabet letters with the respective gestures in a visual and straightforward way, facilitating the learning process.

Abstract
In the last 30 years, several academic and commercial projects have explored the context-awareness concept in multiple domains. Ubiquitous computing and ambient intelligence are features associated with the 4th generation industry empowering space to interact and respond appropriately according to context. In the scope of Industry 4.0, context-aware systems aim to improve productivity in smart factories and offer assistance to workers through services, applications, and devices, delivering functionalities and contextualised content. This article, through descriptive research, discusses the concepts related to context, presents and analyses projects related to ubiquitous computing and associated with Industry 4.0, and discusses the main challenges in systems and applications development to support intelligent environments for increased productivity, supporting informed decision-making in the factories of the future. The study results indicate that many research questions regarding the analysed projects remain the same, leading the research in the context-aware systems area to minimise issues related to context-aware features, improving the incorporation of Industry 4.0 paradigm concepts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Renewable energy sources have transformed the electricity market, allowing virtual power players or aggregators to participate and benefit from selling surplus energy. However, meeting demand and ensuring energy production stability can be challenging due to the intermittent nature of renewable sources. Accurate forecasting of energy consumption, generation, and electricity prices is critical to address these issues. Moreover, the selection of the best algorithm for forecasting is usually based on the predictions’ accuracy, neglecting other factors such as the impact of errors on the real context. This paper presents a study on the economic risk of price forecasting errors on the electricity market’s trading. For this, a simulation model is proposed to analyze the deviations between actual and predicted prices and how these deviations can affect trading in the electricity market, where the main purpose is to maximize profit, depending on whether the player is buying or selling electricity. The economic risk analysis and the predictions scores are used to improve the forecasts, using an approach based on reinforcement learning to evaluating and selecting which models demonstrated better performance in past transactions. The study involved simulating an aggregator’s transactions in the Iberian electricity market for two consecutive days in October 2021. Real data from the market operator between 2020 and 2021 and seven forecasting models were used. The findings showed that errors have a significant impact on profit. Including the economic impact analysis and score evaluation of forecasting methods to determine which method can offer better results has proven to be a viable approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.