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The present paper describes the results of a research study performed in the context of implementation of SPOC courses in Basics of programming and Information technology and information science as part of university training of future teachers of technical and informatics subjects. The aim of the research was to determine which groups of students could be identified by success predictors in the SPOC course. The design of the research was based on quantitative data collection and assessment methods. The survey was performed by means of a questionnaire designed by the authors and involving 44 respondents. The data obtained were analysed using multidimensional statistical data, especially the cluster analysis methods. The results suggested that the respondents could be classified by 11 predictors into two distinctive groups.

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Some analyses state that buildings contribute to overall energy consumption by 20–40%, which, in the context of the recent geopolitical energy crisis, makes them a critical issue to study. Finding solutions for better energy management in buildings can have a significant impact on the energy sector, thus reducing EU energy dependencies and contributing to the fulfillment of the REPowerEU goals. This paper focuses on proposing a simplified model of a residential house considering the main appliances, heating and cooling, a photovoltaic system, and electric vehicle recharging. Weather and solar irradiance forecasts are taken into account. The model predicts the energy demands of a house based on online weather forecasts and the desired indoor temperature. The article also focuses on the analysis of how weather forecast uncertainty affects energy demand prediction. This model can be used to better understand and predict the energy demand of either a single house or a set of houses. A multi-objective optimization approach that takes into account the preferences of users/inhabitants is developed to provide a compromise between the price paid for the electricity and temperature comfort. The authors plan to apply the proposed model to a residential house’s real-time control system. The model will be tuned, its predictions will be tested, and it will be used for energy demand optimization.