Abstract
This study explores models for synthetic data generation of time series. In order to improve the achieved results, i.e., the data generated, new ways of improvement are explored and different models of synthetic data generation are compared. The model addressed in this work is the Generative Adversarial Networks (GANs), known for generating data similar to the original basis data through the training of a generator. The GANs are applied using the datasets of Quinta de Santa Bárbara and the Pinhão region, with the main variables being the Average temperature, Wind direction, Average wind speed, Maximum instantaneous wind speed and Solar radiation. The model allowed to generate missing data in a given period and, in turn, enables to analyze the results and compare them with those of a multiple linear regression method, being able to evaluate the effectiveness of the generated data. In this way, through the study and analysis of the GANs we can see if the model presents effectiveness and accuracy in the synthetic generation of meteorological data. With the proper conclusions of the results, this information can be used in order to improve the search for different models and the ability to generate synthetic time series data, which is representative of the real, original, data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Wind power is becoming an important source of electrical energy production. In an onshore wind farm (WF), the electrical energy is collected at a substation from different wind turbines through electrical cables deployed over ground ditches. This work considers the WF layout design assuming that the substation location and all wind turbine locations are given, and a set of electrical cable types is available. The WF layout problem, taking into account its lifetime and technical constraints, involves selecting the cables to interconnect all wind turbines to the substation and the supporting ditches to minimize the initial investment cost plus the cost of the electrical energy that is lost on the cables over the lifetime of the WF. It is assumed that each ditch can deploy multiple cables, turning this problem into a more complex variant of previously addressed WF layout problems. This variant turns the problem best fitting to the real case and leads to substantial gains in the total cost of the solutions. The problem is defined as an integer linear programming model, which is then strengthened with different sets of valid inequalities. The models are tested with four WFs with up to 115 wind turbines. The computational experiments show that the optimal solutions can be computed with the proposed models for almost all cases. The largest WF was not solved to optimality, but the final relative gaps are small.

Abstract
The increase in renewable generation of a distributed nature has brought significant new challenges to power and energy system management and operation. Self-consumption in buildings is widespread, and with it rises the need for novel, adaptive and intelligent building energy management systems. Although there is already extensive research and development work regarding building energy management solutions, the capabilities for adaptation and contextualization of decisions are still limited. Consequently, this paper proposes a novel contextual rule-based system for energy management in buildings, which incorporates a contextual dimension that enables the adaptability of the system according to diverse contextual situations and the presence of multiple users with different preferences. Results of a case study based on real data show that the contextualization of the energy management process can maintain energy costs as low as possible, while respecting user preferences and guaranteeing their comfort.

Abstract
Despite the ubiquitous term climate neutral cities, there is a distinct lack of quantifiable and meaningful municipal decarbonization goals in terms of the targeted energy balance and composition that collectively connect to national scenarios. In this paper we present a simple but useful allocation approach to derive municipal targets for energy demand reduction and renewable expansion based on national energy transition strategies in combination with local potential estimators. The allocation uses local and regional potential estimates for demand reduction and the expansion of renewables and differentiates resulting municipal needs of action accordingly. The resulting targets are visualized and opened as a decision support system (DSS) on a web-platform to facilitate the discussion on effort sharing and potential realization in the decarbonization of society. With the proposed framework, different national scenarios, and their implications for municipal needs for action can be compared and their implications made explicit.

Abstract
In recent years, as a result of population growth and the strong demand for energy resources, there has been an increase in greenhouse gas emissions. Thus, it is necessary to find solutions to reduce these emissions. This will make the use of electric vehicles (EV) more attractive and reduce the high dependency on internal combustion vehicles. However, the integration of electric vehicles will pose some challenges. For example, it will be necessary to increase the number of fast electric vehicle charging stations (FEVCS) to make electric mobility more attractive. Due to the high power levels involved in these systems, there are voltage drops that affect the voltage profile of some nodes of the distribution networks. This paper presents a methodology based on a genetic algorithm (GA) that is used to find the optimal location of charging stations that cause the minimum impact on the grid voltage profile. Two case studies are considered to evaluate the behavior of the distribution grid with different numbers of EV charging stations connected. From the results obtained, it can be concluded that the GA provides an efficient way to find the best charging station locations, ensuring that the grid voltage profile is within the regulatory limits and that the value of losses is minimized.