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.

This paper presents a detailed description of a dataset derived from the supervisory control and data acquisition (SCADA) system of an onshore wind farm located in Greece, collected over a six-month monitoring period (01/01/2020–30/06/2020). In addition, it provides the source code and scripts implemented for data treatment. The dataset comprises approximately 26,000 measurements from 131 sensors and 24,000 event log entries per turbine across 10 wind turbines (WTs). The sensors are categorized into 12 main categories: Generation, Gear, Nacelle, Rotor, Ambient, Grid, Controller, Blade, HV Transformer, Hour Counters, and Power Metrics, offering a comprehensive view of turbine performance and environmental conditions. Key parameters include pitch angle, power output, generator speed, and bearing temperature, alongside detailed event data such as extreme wind direction and alarm logs. The data were collected through an infrastructure that included sensors, data loggers, interfaces supporting protocols for data accessing and exchange, and a centralized SCADA database. Quality assessments ensured accuracy and consistency, with measures to standardize parameters, correct timestamp errors, and flag missing values. This dataset is designed to support applications such as wind energy forecasting, real-time operational monitoring, and predictive maintenance model development, providing a valuable resource for advancing research and operational improvements in renewable energy management.

This paper presents a dataset obtained from a centrifugal compressor in an oil refinery located in Greece. This set of measurements, collected over a period of one year (01/01/2022-31/12/2022), was derived from the refinery’s distributed control system (DCS) and was used for monitoring the compressor’s operation, providing a detailed and comprehensive understanding of its performance. Key variables such as turbine axial displacement and oil temperature are monitored, offering invaluable insights into the compressor’s behaviour. These variables are monitored through 25 sensors and the dataset contains approximately 35036 readings for each one of them. The validation of the data is important for the optimal operation of the compressor; thus, the refinery’s industrial control systems validate data to ensure accuracy, consistency, and reliability for control and analysis. Validation methods include flagging anomalies, checking process variable ranges, and identifying noise or signal loss. The sensors’ readings of the examined dataset were compared to recommended operating ranges for the identification of patterns across the variables. The comparison revealed that in general the compressor exhibited stable behavior thus the data are indicative of normal operation with minimal deviations from expected performance. Beyond its on-site practical applications, this dataset serves as a valuable resource for academic research in the field of refinery compressors. Researchers can leverage machine learning techniques to explore new ways for improving compressor efficiency, reliability, and control.

Secure, sovereign data sharing is essential for the digital transformation of industrial sectors, yet designing sustainable Business Models (BMs) for multi-stakeholder Data Spaces remains a significant challenge. This study situates BM innovation for Data Spaces in the context of European manufacturing, addressing regulatory, technological, and economic constraints. The methods involve mapping stakeholder needs to platform capabilities, and systematically developing and validating revenue and value-capture models using established frameworks and real-world use cases. Results show that collaborative, modular BMs—built on principles of data sovereignty, interoperability, and trust—enable the creation and distribution of economic and strategic value for operators, data providers, and consumers. Application in predictive maintenance use cases for refineries and wind farms validates these models, demonstrating cost reductions, improved operational efficiency, and compliance with current European data governance standards. The findings suggest that viable BMs for industrial Data Spaces hinge on inclusive stakeholder participation, flexible monetization strategies, and continuous adaptation to regulatory environments. These insights provide practical guidance for stakeholders seeking to realize the full value of data-driven manufacturing ecosystems.

The growing interest in using data in the manufacturing sector has led to an increased need for efficient and secure data sharing among stakeholders. However, technical, business, and regulatory barriers often prevent the full exploitation of manufacturing data. To address these challenges, the European Commission (EC) has introduced the concept of Common European Data Spaces (CEDS) as part of its broader data strategy. These data spaces aim to ensure data sovereignty and interoperability, in compliance with European regulations, facilitating a trusted environment for data exchange. This paper explores the deployment of two key European-funded projects, UNDERPIN and SM4RTENANCE, which are designed to accelerate the development of the CEDS for the manufacturing sector. These initiatives represent the current efforts towards enabling system integration and strengthening data security, while aiming to enhance cross sector collaboration within the industrial ecosystem. The development of UNDERPIN and SM4RTENANCE data spaces play a crucial role in overcoming technical and business barriers, enabling organizations to unlock the full potential of data-driven innovation and improve operational excellence.

Secure, sovereign data sharing is essential for the digital transformation of industrial sectors, yet designing sustainable Business Models (BMs) for multi-stakeholder Data Spaces remains a significant challenge. This study situates BM innovation for Data Spaces in the context of European manufacturing, addressing regulatory, technological, and economic constraints. The methods involve mapping stakeholder needs to platform capabilities, and systematically developing and validating revenue and value-capture models using established frameworks and real-world use cases. Results show that collaborative, modular BMs—built on principles of data sovereignty, interoperability, and trust—enable the creation and distribution of economic and strategic value for operators, data providers, and consumers. Application in predictive maintenance use cases for refineries and wind farms validates these models, demonstrating cost reductions, improved operational efficiency, and compliance with current European data governance standards. The findings suggest that viable BMs for industrial Data Spaces hinge on inclusive stakeholder participation, flexible monetization strategies, and continuous adaptation to regulatory environments. These insights provide practical guidance for stakeholders seeking to realize the full value of data-driven manufacturing ecosystems.