As cities grow rapidly and energy needs increase, shaping an effective energy transition is a top priority towards urban sustainability and smart development. This study attempts to answer three key research questions that can help city authorities, planners and interested agents simplify and increase the transparency of Key Performance Indicators (KPIs) selection for smart city and communities (SCC) projects focusing on energy transition and creation of Positive Energy Districts (PEDs): Question 1: “What resources are available for extracting such KPIs?”; Question 2: “Which of those KPIs are the most suitable for assessing the energy transition of smart city projects and PED-related developments?” and Question 3: “How can a project-specific shortlist of KPIs be developed?”. Answering these questions can also serve as a major first step towards a “universal” KPI selection procedure. In line with this purpose, an experiential approach is presented, capitalizing on knowledge and lessons learned from an ongoing smart city project in Europe (POCITYF) that focuses on PED deployment. Under this framework, a) a review of smart city KPI frameworks has been conducted, resulting in a pool of 258 indicators that can potentially be adopted by smart city projects; b) eight key dimensions of evaluations were extracted, setting a holistic performance framework relevant to SCCs; c) a detailed evaluation process including pre-determined criteria and city-needs feedback was applied to shortlist the KPI pool, leading to a ready-to-be-used, project-specific list of 63 KPIs and d) KPIs were sorted and analyzed in different granularity levels to further facilitate the monitoring procedure. The experiential procedure presented in this study can be easily adapted to the needs of every smart city project, serving as a recommendation guide.

The current energy crisis raised concern about the lack of electricity during the wintertime, especially that consumption should be cut at peak consumption hours. For the building owners, this is visible as rising electricity prices. Availability of near real-time data on energy performance is opening new opportunities to optimize energy flexibility capabilities of buildings. This paper presents a reinforcement learning (RL)-based method to control the heating for minimizing the heating electricity cost and shifting the electricity usage away from peak demand hours. Simulations are carried out with electrically heated single-family houses. The results indicate that with RL, in the case of varying electricity prices, it is possible to save money and keep the indoor thermal comfort at an appropriate level.

Modern knowledge work is highly intense and demanding, exposing workers to long-term psychosocial stress. In order to address the problem, stress detection technologies have been developed, enabling the continuous assessment of personal stress based on multimodal sensor data. However, stakeholders lack insights into how employees perceive different monitoring technologies and whether they are willing to share stress-indicative data in order to sustain well-being at the individual, team, and organizational levels in the knowledge work context. To fill this research gap, we developed a theoretical model for knowledge workers’ interest in sharing their stress-indicative data collected with unobtrusive sensors and examined it empirically using structural equation modeling (SEM) with a survey of 181 European knowledge workers. The results did not show statistically significant privacy concerns regarding environmental sensors such as air quality, sound level, and motion sensors. On the other hand, concerns about more privacy-sensitive methods such as tracking personal device usage patterns did not prevent user acceptance nor intent to share data. Overall, knowledge workers were highly interested in employing stress monitoring technologies to measure their stress levels and receive information about their personal well-being. The results validate the willingness to accept the unobtrusive, continuous stress detection in the context of knowledge work.

The availability of near-real-time data on energy performance is opening new opportunities to optimize buildings’ energy efficiency and flexibility capabilities and to support the decision-making and planning process of building retrofitting infrastructure investment. Existing tools can support retrofitting design and energy performance contracting. However, there are well-recognized shortcomings of these tools related to their usability, complexity, and ability to perform calculations based on the real-time energy performance of buildings. To address this gap, the advanced retrofitting decision support tool is developed and presented in this study. The strengths of our solution rely on easy usability, accuracy, and transparency of results. The automatic collection of real-time building energy consumption data gathered from the building management systems, combined with data analytics techniques, ensures ease of use and quickness of calculation. These results support step-by-step thinking for retrofitting design and hopefully enable a larger utilization rate for deep building retrofits.

Positive Energy Districts (PEDs) are a promising approach to urban energy transformation, aiming to optimize local energy systems and deliver environmental, social and economic benefits. However, their effectiveness and justification for investment rely on understanding the additional value they provide (additionality) in comparison to current policies and planning methods. The additionality perspective is not used yet in current evaluations of PED demonstrations and pilots. Therefore, this paper introduces the concept of additionality in the evaluation of PEDs, focusing on the additional benefits they bring and the circumstances under which they are most effective. We discuss the additionality of PEDs in addressing the challenges of climate neutrality and energy system transformation in three European cities that are funded by the European Commission’s H2020 Programme. It should be noted that given the ongoing status of these projects, the assessment is mainly based on preliminary results, as monitoring is still ongoing and quantitative results are not yet available. The paper discusses the drivers and barriers specific to PEDs, and highlights the challenges posed by technical complexities, financing aspects and social and legal restrictions. Conclusions are drawn regarding the concept of additionality and its implications for the wider development of PEDs as a response to the challenges of climate neutrality and energy system transformation in cities. We conclude that the additionality perspective provides valuable insights into the impact and potential of PEDs for societal goals and recommend this approach for use in the final evaluation of R&I projects involving PEDs using actual monitored data on PEDs.