Abstract

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
Local electricity markets are emerging as a viable solution to overcome the challenges brought by the large penetration of distributed renewable generation and the need to put consumers as central players in the system, with an active and dynamic role. Although there is significant literature addressing the topic of local electricity markets, this is still a rather new and emerging topic. Hence, this study provides an overall view of this domain and a perspective on future needs and challenges that must be addressed. This review introduces the most important concepts in the local electricity market domain, provides an analysis on the different policy and regulatory framework, exposes the most relevant worldwide initiatives related to the field implementation, and scrutinizes the alternative local market models proposed in the literature. The discussion puts forth the main benefits and barriers of the currently proposed local market models, and the expected impact of their widespread implementation. The review is concluded with the wrap-up and discussion on the most relevant paths for future research and development in this field of study.

Abstract
Agriculture being an essential activity sector for the survival and prosperity of humanity, it is fundamental to use sustainable technologies in this field. With this in mind, some statistical data are analyzed regarding the food price rise and sustainable development indicators, with a special focus on the Portugal region. It is determined that one of the main factors that influences agriculture's success is the soil's characteristics, namely in terms of moisture and nutrients. In this regard, irrigation processes have become indispensable, and their technological management brings countless economic advantages. Like other branches of agriculture, the wine sector needs an adequate concentration of nutrients and moisture in the soil to provide the most efficient results, considering the appropriate and intelligent use of available water and energy resources. Given these facts, the use of renewable energies is a very important aspect of this study, which also synthesizes the main irrigation methods and examines the importance of evaluating the evapotranspiration of crops. Furthermore, the control of irrigation processes and the implementation of optimization and resource management models are of utmost importance to allow maximum efficiency and sustainability in this field.

Abstract
In recent years, there has been a significant growth in the use of technologies that rely on natural resources (wind, solar, etc.) as primary sources of energy. The generation originating from renewable sources brings an increased need for adaptation in power electrical systems. Predicting the amount of energy produced by these technologies is a complex task due to the uncertainty associated with natural resources. This uncertainty hinders decision-making, both at the system level and for consumers themselves who are increasingly using this type of technology for self-consumption. This study focuses on classifying solar intensity using imbalanced data, which means that some of the data categories are more prevalent than others. Oversampling techniques are be employed to increase the amount of data, thereby allowing for balanced training data and improving the performance of prediction models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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.