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
Futures contracts are a valuable market option for electricity negotiating players, as they enable reducing the risk associated to the day-ahead market volatility. The price defined in these contracts is, however, itself subject to a degree of uncertainty; thereby turning price forecasting models into attractive assets for the involved players. This paper proposes a model for futures contracts price forecasting, using artificial neural networks. The proposed model is based on the results of a data analysis using the spearman rank correlation coefficient. From this analysis, the most relevant variables to be considered in the training process are identified. Results show that the proposed model for monthly average electricity price forecast is able to achieve very low forecasting errors.

Abstract
Agent-based simulation tools have found many applications in the field of Power and Energy Systems, as they can model and analyze the complex synergies of dynamic and continuously evolving systems. While some studies have been done w.r.t. simulation and decision support for electricity markets and smart grids, there is still a generalized limitation referring to the significant lack of interoperability between independently developed systems, hindering the task of addressing all the relevant existing interrelationships. This work presents the Semantic Services Catalog (SSC), developed and implemented for the automatic registry, discovery, composition, and invocation of web and agent-based services. By adding a semantic layer to the description of different types of services, this tool supports the interaction between heterogeneous multiagent systems and web services with distinct capabilities that complement each other. The case study confirms the applicability of the developed work, wherein multiple simulation and decision-support tools work together managing a microgrid of residential and office buildings. Using SSC, besides discovering each other, agents also learn about the ontologies and languages to use to communicate with each other effectively. © 2021, Springer Nature Switzerland AG.

Abstract
Industry 4.0 was publicly introduced in Germany in 2011 and is known as the fourth industrial revolution, whose goal is to improve manufacturing processes and increase the competitiveness of the manufacturing industry. Industry 4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity in industry. This paper aims to explore the use of context-aware applications in Industry 4.0 in order to assist workers in decision making and thus improve the performance of factory production lines. This literature review is part of the project “Continental AA’s Factory of the Future” (Continental FoF) and will integrate a context-aware system in Industry 4.0 of the mentioned company, which is a manufacturer of radio frequency devices for the automotive industry. This systematic literature review identifies, from the researched solutions, the concept of context and context-awareness, the main technologies used in context-aware systems, how context management is performed, as well as the most used integration and communication protocols. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.