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
Virtual assistants offer a new type of solution to handle interaction between human and machine and can be applied in various business contexts such as Industry or Education. When designing and building a virtual assistant the developers must ensure a set of parameters to achieve a good solution. Various platforms and frameworks emerged to allow developers to create virtual assistant solutions easier and faster. This paper provides a review of available platforms and frameworks used by authors to create their own solutions in different areas. Big tech companies like Google with Dialogflow, IBM with Watson Assistant and Microsoft with Bot Framework, present mature solutions to build virtual assistants that provide to the developer all components of the basic architecture to build a fast and solid solution. Open-Source solutions focus on providing to the developer the main components to build a virtual assistant, namely language understanding and response generation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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
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.