FAIR Metadata Standards for Low Carbon Energy Research—A Review of Practices and How to Advance

The principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) have been put forward to guide optimal sharing of data. The potential for industrial and social innovation is vast. Domain-specific metadata standards are crucial in this context, but are widely missing in the energy sector. This report provides a collaborative response from the low carbon energy research community for addressing the necessity of advancing FAIR metadata standards. We review and test existing metadata practices in the domain based on a series of community workshops. We reflect the perspectives of energy data stakeholders. The outcome is reported in terms of challenges and elicits recommendations for advancing FAIR metadata standards in the energy domain across a broad spectrum of stakeholders

A methodology for designing layered ontology structures

Semantic ontologies represent the knowledge from different domains, which is used as a knowledge base by intelligent agents. The creation of ontologies by different developers leads to heterogeneous ontologies, which hampers the interoperability between knowledge-based applications. This interoperability is achieved through global ontologies, which provide a common domain representation. Global ontologies must provide a balance of reusability-usability to minimise the ontology effort in different applications. To achieve this balance, ontology design methodologies focus on designing layered ontologies that classify into abstraction layers the domain knowledge relevant to many applications and the knowledge relevant to specific applications. During the design of the layered ontology structure, the domain knowledge classification is performed from scratch by domain experts and ontology engineers in collaboration with application stakeholders. Hence, the design of reusable and usable ontologies in complex domains takes a significant effort. Software Product Line (SPL) design techniques can be applied to facilitate the domain knowledge classification by analysing the knowledge similarities/differences of existing ontologies. In this context, this thesis aims to define new methodological guidelines to design layered ontology structures that enable to classify the domain knowledge taking as reference existing ontologies, and to apply these guidelines to enable the development of reusable and usable ontologies in complex domains. The MODDALS methodology guides the design of layered ontology structures for reusable and usable ontologies. It brings together SPL engineering techniques and ontology design techniques to enable the classification of the domain knowledge by exploiting the knowledge similarities/differences of existing ontologies. MODDALS eases the design of the layered ontology structure. The MODDALS methodology was evaluated by applying it to design the layered structure of a reusable and usable global ontology for the energy domain. The designed layered structure was taken as reference to develop the ontology. The resulting ontology simplifies the ontology reuse process in different applications. In particular, it reduced the average ontology reuse time by 0.5 and 1.2 person-hours in in two different applications in comparison with a global energy ontology which does not follow a layered structure.Ontologia semantikoak datu domeinu ezberdinen ezagutza irudikatzen dute, agente adimendunek jakintza oinarri bezala erabiltzen dutena. Ontologiak ingeniari desberdinek garatzen dituzte eta heterogeneoak dira, aplikazioen arteko komunikazioa oztopatuz. Komunikazio hau ontologia globalen bidez lortzen da, domeinuaren errepresentazio komun bat ematen baitute. Ontologia globalek berrerabilgarritasunerabilgarritasun oreka eman behar dute aplikazio desberdinetan berrerabiltzeko ahalegina murrizteko. Horretarako, ontologia diseinu metodologiek aplikazio askok erabiltzen duten eta aplikazio zehatzetarako garrantzitsua den ezagutza abstrakzio geruzetan sailkatzea proposatzen dute. Geruza egituraren diseinuan zehar, domeinuko adituek eta ontologiako ingeniariek hutsetik sailkatzen dute jakintza, domeinu konplexuetan ontologia berrerabilgarriak eta erabilgarrien diseinu ahalegina areagotuz. Software produktu lerroak diseinatzeko erabiltzen diren teknikak jakintza sailkatzea erraztu ahal dute, ontologien ezagutza antzekotasunak edo desberdintasunak aztertuz. Testuinguru honetan, honakoa da tesiaren helburua: ezagutza garatutako ontologien arabera sailkatzen duen ontologia berrerabilgarri eta erabilgarrien geruza egitura diseinatzeko metodologia bat garatzea; baita metodologia aplikatu ere, ontologia berrerabilgarri eta erabilgarriak domeinu konplexuetan garatu ahal izateko. MODDALS metodologiak ontologia berrerabilgarri eta erabilgarrien abstrakzio geruzak nola diseinatu azaltzen du. MODDALS-ek software produktu lerro eta ontologia diseinu teknikak aplikatzen ditu ezagutza garatuta dauden ontologien antzekotasunen/desberdintasunen arabera sailkatzeko. Planteamendu honek geruza egitura diseinua errazten du. MODDALS ebaluatu da energia domeinurako ontologia berrerabilgarri eta erabilgarri baten egitura diseinatzeko aplikatuz. Diseinatutako geruza egitura erreferentzia gisa hartu da ontologia gartzeko. Egitura onekin, garatutako ontologia berrerabiltzea errazten du aplikazio desberdinetan. Konkretuki, garatutako ontologiak berrerabilpen denbora 0.5 eta 1.2 pertsona-orduetan murriztu du bi aplikazioetan; geruza egitura jarraitzen ez duen ontologia batekin alderatuz.Las ontologías semánticas representan el conocimiento de diferentes dominios, utilizado como base de conocimiento por agentes inteligentes. Las ontologías son desarrolladas por diferentes ingenieros y son heterogéneas, afectando a la interoperabilidad entre aplicaciones. Esta interoperabilidad se logra mediante ontologías globales que proporcionan una representación común del dominio, las cuales deben proporcionar un balance de reusabilidad-usabilidad para minimizar el esfuerzo de reutilización en diferentes aplicaciones. Para lograr este balance, las metodologías de diseño de ontologías proponen clasificar en capas de abstracción el conocimiento del dominio común a muchas aplicaciones y el que es relevante para aplicaciones específicas. Durante el diseño de la estructura de capas, el conocimiento se clasifica partiendo de cero por expertos del dominio e ingenieros de ontologías. Por lo tanto, el diseño de ontologías reusables y usables en dominios complejos requiere un gran esfuerzo. Las técnicas de diseño de líneas de producto de software pueden facilitar la clasificación del conocimiento analizando las similitudes/diferencias de conocimiento de ontologías existentes. En este contexto, el objetivo de la tesis es crear una metodología de diseño de la estructura de capas para ontologías que permita clasificar el conocimiento tomando como referencia ontologías existentes, y aplicar esta metodología para poder desarrollar ontologías reusables y usables en dominios complejos. La metodología MODDALS explica cómo diseñar estructuras de capas para ontologías reusables y usables. MODDALS adopta técnicas de diseño de líneas de producto en combinación con técnicas de diseño de ontologías para clasificar el conocimiento basándose en las similitudes/diferencias de ontologías existentes. Este enfoque facilita el diseño de la estructura de capas de la ontología. La metodología MODDALS se ha evaluado aplicándola para diseñar la estructura de capas de una ontología global reusable y usable para el dominio de la energía. La estructura de capas diseñada se ha tomado como referencia para desarrollar la ontología. Con esta estructura, la ontología resultante simplifica la reutilización de ontologías en diferentes aplicaciones. En concreto, la ontología redujo el tiempo de reutilización en 0.5 y 1.2 personas-hora en dos aplicaciones respecto a una ontología global que no sigue una estructura por capas

From efficiency to reduction

This book presents the results of the international research project CODALoop: Community Data Loop for Energy Conscious Lifestyles. It dissects the energy practices that make urban households demanding energy in their daily life and reveals the pathway towards reducing this energy demand. To unpack energy practices, the authors of this volume move away from efficiency problems studying the interaction between human and new technologies. Instead, they use a repertoire of different analytical instruments to study how interaction between humans, and between humans and data, change the social norms that shape energy needs. The volume offers a synthesis of a cross- disciplinary study of energy reduction carried out in three different countries through multiple methodological approaches. The project at the source of the book was funded under the Joint Program Initiative 'Urban Europe' and the ERA-net framework. To unpack energy practices, the authors of this volume move away from efficiency problems studying the interaction between human and new technologies. Instead, they use a repertoire of different analytical instruments to study how interaction between humans, and between humans and data, change the social norms that shape energy needs. The volume offers a synthesis of a cross- disciplinary study of energy reduction carried out in three different countries through multiple methodological approaches. The project at the source of the book was funded under the Joint Program Initiative 'Urban Europe' and the ERA-net framework

Scale aware modeling and monitoring of the urban energy chain

With energy modeling at different complexity levels for smart cities and the concurrent data availability revolution from connected devices, a steady surge in demand for spatial knowledge has been observed in the energy sector. This transformation occurs in population centers focused on efficient energy use and quality of life. Energy-related services play an essential role in this mix, as they facilitate or interact with all other city services. This trend is primarily driven by the current age of the Ger.: Energiewende or energy transition, a worldwide push towards renewable energy sources, increased energy use efficiency, and local energy production that requires precise estimates of local energy demand and production. This shift in the energy market occurs as the world becomes aware of human-induced climate change, to which the building stock has a significant contribution (40% in the European Union). At the current rate of refurbishment and building replacement, of the buildings existing in 2050 in the European Union, 75% would not be classified as energy-efficient. That means that substantial structural change in the built environment and the energy chain is required to achieve EU-wide goals concerning environmental and energy policy. These objectives provide strong motivation for this thesis work and are generally made possible by energy monitoring and modeling activities that estimate the urban energy needs and quantify the impact of refurbishment measures. To this end, a modeling library called aEneAs was developed in the scope of this thesis that can perform city-wide building energy modeling. The library performs its tasks at the level of a single building and was a first in its field, using standardized spatial energy data structures that allow for portability from one city to another. For data input, extensive use was made of digital twins provided from CAD, BIM, GIS, architectural models, and a plethora of energy data sources. The library first quantifies primary thermal energy demand and then the impact of refurbishment measures. Lastly, it estimates the potential of renewable energy production from solar radiation. aEneAs also includes network modeling components that consider energy distribution in the given context, showing a path toward data modeling and simulation required for distributed energy production at the neighborhood and district level. In order to validate modeling activities in solar radiation and green façade and roof installations, six spatial models were coupled with sensor installations. These digital twins are included in three experiments that highlight this monitoring side of the energy chain and portray energy-related use cases that utilize the spatially enabled web services SOS-SES-WNS, SensorThingsAPI, and FIWARE. To this author\u27s knowledge, this is the first work that surveys the capabilities of these three solutions in a unifying context, each having its specific design mindset. The modeling and monitoring activity and their corresponding literature review indicated gaps in scientific knowledge concerning data science in urban energy modeling. First, a lack of standardization regarding the spatial scales at which data is stored and used in urban energy modeling was observed. In order to identify the appropriate spatial levels for modeling and data aggregation, scale is explored in-depth in the given context and defined as a byproduct of resolution and extent, with ranges provided for both parameters. To that end, a survey of the encountered spatial scales and actors in six different geographical and cultural settings was performed. The information from this survey was used to put forth a standardized spatial scales definition and create a scale-dependent ontology for use in urban energy modeling. The ontology also provides spatially enabled persistent identifiers that resolve issues encountered with object relationships in modeling for inheritance, dependency, and association. The same survey also reveals two significant issues with data in urban energy modeling. These are data consistency across spatial scales and urban fabric contiguity. The impact of these issues and different solutions such as data generalization are explored in the thesis. Further advancement of scientific knowledge is provided specifically with spatial standards and spatial data infrastructure in urban energy modeling. A review of use cases in the urban energy chain and a taxonomy of the standards were carried out. These provide fundamental input for another piece of this thesis: inclusive software architecture methods that promote data integration and allow for external connectivity to modern and legacy systems. In order to reduce time-costly extraction, transformation, and load processes, databases and web services to ferry data to and from separate data sources were used. As a result, the spatial models become central linking elements of the different types of energy-related data in a novel perspective that differs from the traditional one, where spatial data tends to be non-interoperable / not linked with other data types. These distinct data fusion approaches provide flexibility in an energy chain environment with inconsistent data structures and software. Furthermore, the knowledge gathered from the experiments presented in this thesis is provided as a synopsis of good practices

Smart and Sustainable Planning for Cities and Regions

This open access book offers a selection of research papers and case studies presented at the 3rd international conference “Smart and Sustainable Planning for Cities and Regions”, held in December 2019 in Bolzano, Italy, and explores the concept of smart and sustainable planning, including top contributions from academics, policy makers, consultants and other professionals. Innovation processes such as co-design and co-creation help establish collaborations that engage with stakeholders in a trustworthy and transparent environment while answering the need for new value propositions. The importance of an integrated, holistic approach is widely recognized to break down silos in local government, in particular, when aimed at achieving a better integration of climate-energy planning. Despite the ongoing urbanization and polarization processes, new synergies between urban and rural areas emerge, linking development opportunities to intrinsic cultural, natural and man-made landscape values. The increasing availability of big, real-time urban data and advanced ICT facilitates frequent assessment and continuous monitoring of performances, while allowing fine-tuning as needed. This is valid not only for individual projects but also on a wider scale. In addition, and circling back to the first point, (big) urban data and ICT can be of enormous help in facilitating engagement and co-creation by raising awareness and by providing insight into the local consequences of specific plans. However, this potential is not yet fully exploited in standard processes and procedures, which can therefore lack the agility and flexibility to keep up with the pulse of the city and dynamics of society. The book provides a multi-disciplinary outlook based on experience to orient the reader in the giant galaxy of smart and sustainable planning, support the transposition of research into practice, scale up visionary approaches and design groundbreaking planning policies and tools

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity