Abstract
The increasing complexity of integrated energy systems with the electric power grid requires innovative control solutions for efficient management of smart buildings and distributed energy resources. Accurately predicting weather conditions and electricity demand is crucial to make such informed decisions. Machine learning has emerged as a powerful solution to enhance prediction accuracy by harnessing advanced algorithms, but often requires complex parameterizations and ongoing model updates. The Lawrence Berkeley National Laboratory's Autonomous Forecast Framework (AFF) was developed to greatly simplify this process, providing reliable and accurate forecasts with minimal user interaction, by automatically selecting the best model out of a library of candidate models. This work expands on the AFF by not only selecting the best model, but assembling a blend of multiple models into a hybrid forecast model. The validation within this work has shown that this combination of models outperformed the selected best model of the AFF 31%, while providing greater resilience to individual model's forecast error.

Abstract
The number of electric vehicles is increasing progressively for various reasons, including economic and environmental factors. There has also been a technological development regarding both the operation and charging of these vehicles. Therefore, it is very important to reinforce the charging infrastructure, which can be optimised through the application of computational tools. There are several approaches that should be considered when trying to find the best location for electric vehicles charging stations. In the literature, different methods are described that can be applied to address this specific issue, including optimisation methods and decision-making techniques such as multicriteria analysis. One of the possible limitations of these methods is that they may not consider all perspectives of the various entities involved, potentially resulting in solutions that do not fully represent the optimal outcome; nevertheless, they provide invaluable information that can be applied in the development of integrative models and potentially more comprehensive ones. This article presents a research and discussion on the most commonly used decision models for this issue, considering optimisation models and multi-criteria decision-making strategies for the adequate planning of EV charging station installation,taking into account the different perspectives of the involved entities.

Abstract
The objective of achieving carbon neutrality by 2050 requires the various sectors of the economy to actively participate in the decarbonisation of all their activities, from production to consumption and product distribution. The vineyard and wine production sector is no exception to this goal. This paper aims to evaluate the feasibility and efficiency that hybrid energy systems based on renewable energy sources, solar photovoltaic (PV) and wind, can contribute to energy efficiency in certain activities related to wine production. In this sense, this study presents results based on linear programming optimisation models, which show how effective they are in minimising the use of energy from the power grid. The results show that renewable hybrid energy systems based on PV and wind are an effective solution for achieving carbon neutrality in some agricultural sectors, particularly winemaking sector. Besides being able to minimise the energy bought from the grid, the hybrid renewable energy system (HRES) is almost self-sufficient, being able to produce 340,232 kWh over 25 years.

Abstract
In the global transformation towards a sustainable energy system, the implementation of energy efficiency measures and demand flexibility play a crucial role. Dynamic window shading of building facades poses a great potential to reduce, shift, and modulate a building's electricity consumption by blocking solar heat gains and thereby avoiding expensive Heating, Ventilation, and Air-Conditioning (HVAC) operation to cool the building. However, the installation of dynamic facade systems is often cost-prohibitive with expensive building wiring and interconnection. An integrated direct current (DC) nanogrid is proposed instead, which eliminates any electrical interconnection, by combining all components - generation, storage, and shading element into a self-contained unit. This study seeks to assess the unique design criteria of such Integrated Facade Node (IFN) system given infrequent but high-power use, coincidence of dynamic facade operation with solar renewable photovoltaic (PV) power generation, and unusual placement of the PV generator along the building facade. Optimal IFN sizes based on a deterministic sizing algorithm for a south facing building perimeter are analyzed and installation cost savings of $64,000 (65%) for a medium office building, with the potential to increase up to 91%, are presented.

Abstract
This article presents a comprehensive state of the art analysis of the challenging domain of synthetic data generation. Focusing on the problem of synthetic data generation, the paper explores various difficulties that are identified, especially in real-world problems such as those is the scope of power and, energy systems, including the amount of data, data privacy concerns, temporal considerations, dynamic generation, delays, and failures. The investigation delves into the multifaceted nature of the challenges presented by these factors in the synthesis process. The review thoroughly examines different models used in synthetic data generation, covering Generative Adversarial Networks (GANs), Variational Autoencoder (VAE), Synthetic Minority Oversampling Technique (SMOTE), Data Synthesizer (DS) and E. Non-Parametric SynthPop (SP-NP). Each model is dissected with respect to its advantages, disadvantages, and applicability in different data generation scenarios. Special attention is paid to the nuanced aspects of dynamic data generation and the mitigation of challenges such as delays and failures. The insights drawn from this review contribute to a deeper understanding of the landscape around synthetic data generation, providing a valuable resource for researchers, practitioners, and stakeholders who aim to harness the potential of synthetic data in addressing real-world data challenges. The paper concludes by outlining possible avenues for future research and development in this ever-evolving field. © 2024 IEEE.

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.