To achieve the “well below 2 degrees” targets, a new ecosystem needs to be defined where citizens become more active, co-managing with relevant stakeholders, the government, and third parties. This means moving from the traditional concept of citizens-as-consumers towards energy citizenship. Positive Energy Districts (PEDs) will be the test-bed area where this transformation will take place through social, technological, and governance innovation. This paper focuses on benefits and barriers towards energy citizenships and gathers a diverse set of experiences for the definition of PEDs and Local Energy Markets from the Horizon2020 Smart Cities and Communities projects: Making City, Pocityf, and Atelier.
Key takeaways from the project underscore the importance of fostering long-term collaborations between technical experts, communities, and institutional partners. By integrating technical innovation with human-centred design, the SUSTENANCE project has not only advanced renewable energy adoption but also established a framework for empowering communities to actively participate in sustainable energy transitions. Moving forward, the lessons learned, and solutions developed provide a solid foundation for addressing future challenges in energy system decarbonization and resilience.
The following report aims to introduce the main me2 specifications, and to describe the requirements needed to develop the me2 project. Me2 is a technological platform where the behaviours related to energy consumption could be monitored, and also to increase the energy efficiency.In order to have a better understanding about the use of that kind of platforms, a brief literature review is firstly presented, where some of the main behaviour changing mechanisms practices are highlighted. Also, a policy analysis was developed to give an extended overview of the existing market structures and barriers, as well as, the technical features that are relevant for the development of a venture like me2.The report will end with a detailed description of what the me2 user will be like. This information is mostly based on the pre-pilot survey and on a cross-cultural analysis between Portugal and the Netherlands. This comparison is fundamental for a better understanding about the target community used in this project. Concerning to the functional systems requirements, they are also described in this report, giving special attention to what is called me2 Logic, that includes the front-end platform, back-end activities, and the algorithms to user engagement.Therefore, this report delivers, in a very detailed way, all the reviewed information and procedures needed to be determined prior to the platform’s establishment, and regarding its implementation for the project’s first pilot in Lisbon.
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
