• Energy Research
• ZENODO close

This deliverable presents (1) an analysis of the interview results conducted within the energy communities to understand how energy information is being understood and used by different stakeholders, and (2) an analysis using topic modelling to identify the role of social media in energy informatics.

The digitalisation of the energy domain can bring forth numerous aspects of the energy transition that can boost the emergence of energy citizenship, information sharing, and improved decision-making processes. However, this is premised on citizens being able to make sense of (digital) information. Hence, this paper proposes a link between energy informatics and energy citizenship via energy literacy, considering the cognitive and affective aspects of energy literacy and their relation to behaviour and action. By doing so, this paper aims to understand how the use of energy-related information and social media within five different case studies from the GRETA project can impel energy citizenship. This paper approaches this rationale through different means: (a) structured interviews to understand how citizens understand and make use of energy information within the case studies; (b) topic modelling on the content of those interviews to identify common factors that might spur on hinder behaviour change towards energy citizenship; and (c) social media content analysis to identify key energy-related topics of discussions among citizens around the globe and assess the role of social media as a tool for energy citizenship. As a result, this paper identified some key takeaways to improve the delivery of energy-related information to energy citizens for enhanced energy citizenship. These takeaways allow to conclude that it is fundamental to surpass the formal boundaries of techno-economic constructs and start addressing qualitative/subjective constructs (e.g., emotions, affections, and feelings) to foster energy citizenship. Also, these takeaways could be translated into social mechanism principles in the design of frontend energy-related digital platforms for improved end-user interactions and energy citizenship. Finally, this paper recognised the need to incentivise energy citizens to use social media for consuming energy-related information, and the need to formulate coordinated and coherent response strategies for disseminating energy-related information.

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines.

In this position paper we present IT-centered challenges that lie in designing an architecture for a flexible, open, transferable, and replicable smart city ecosystem spanning a plethora of suppliers and systems. The background is the smart city and energy project +CityxChange. Its vision is to enable the co-creation and development of Positive Energy Blocks in smart sustainable cities. It will include the development of a framework and supporting tools to enable a common energy market, supported by a connected community and city integration. It will explore influences of the energy transition into city operations and urban planning, the integration of e-Mobility as a Service (eMaaS) into positive energy communities, and the growth of local trading markets and new business models. Digitalization, open architectures, and open data need to support these processes for open urban innovation in the ICT-enabled city. This is a post-peer-review, pre-copyedit version of an article published in [Communications in Computer and Information Science ]. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-22482-0_9

Machine learning (ML) models have been widely used in diverse applications of energy systems such as design, modeling, complex mappings, system identification, performance prediction, and load forecasting. In particular, the last two decades has seen a dramatic increase in the development and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a taxonomy of applications and methods. Through a novel search methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and the application area. Furthermore, a comprehensive review of the literature represents an assessment and performance evaluation of the ML models, their applications and a discussion on the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and the generalization ability of the ML models in energy systems using the hybrid and ensemble ML algorithms. ML models are widely used in solar energy and wind energy prediction so that these sustainable energy sources will become more practical and more economic. Energy demand prediction by ML models will also improve our communities’ sustainability.