About this Research Topic
The transition towards sustainable energy systems requires the attainment of ambitious goals including: increasing energy efficiency, increasing renewable energy share and reducing carbon emissions. Energy analytics and informatics research today involves enabling ICT solutions to accomplish these goals.
Over the last few years, the application of data analytics has been boosting multi-disciplinary research across areas such as energy, climate change and sustainability. Furthermore, disruptive paradigms such as Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing data streams together with contextual information, enabling the creation of ecosystems of smart applications vertically integrated in different domains (e.g. energy infrastructure, transportation, industrial processes, etc.). However, the effective exploitation of large-scale data streams and contextual information remains a challenge, especially when there is the necessity of supporting adequate decision-making to accelerate evolution across multiple domains, under uncertainty.
This Research Topic aims to stimulate research on a broad range of topics related to energy analytics and informatics, providing insightful analyses that can foster future collaborative exchange among academia, industry, government and civil society. The editors welcome submissions of theoretical and methodological studies, as well as of empirical research focused on practical applications and case studies, or a combination thereof.
Topics of interest include, but are not limited to, the following issues:
• Open data standards and open science practices in energy research
o Open data standards and their implications for energy research
o Open source modelling projects in the energy sector
o Transparency of modelling methodologies and documentation
• Data science and analytics for energy research
o Component level analytics (individual technologies)
o Building system level analytics
o Neighbourhood/city scale level analytics and interaction with infrastructures
• Multi-scale analysis techniques
o Scalability to enable effective transitions of energy systems at multiple scales and use of similar
techniques for multiple purposes
o Hybrid physical/statistical models of energy systems, considering both technical/physical factors
and social factors (human influenced)
o Models for vertical and horizontal integration of information
• Insights on the use of multi-scale analysis techniques to support sustainable energy transitions in the
built environment
o Suitability for multiple purposes across building life cycle phases, enabling or supporting innovative
business models aimed at efficiency and sustainability
o Continuity in the use of multiple types of interconnected models across life cycle phases and
continuous improvement strategies using feedback from data
o Multi-scale applications of energy data analytics in buildings, addressing multiple purposes, spatial
and temporal scales as well as different life-cycle phases
Over the last few years, the application of data analytics has been boosting multi-disciplinary research across areas such as energy, climate change and sustainability. Furthermore, disruptive paradigms such as Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing data streams together with contextual information, enabling the creation of ecosystems of smart applications vertically integrated in different domains (e.g. energy infrastructure, transportation, industrial processes, etc.). However, the effective exploitation of large-scale data streams and contextual information remains a challenge, especially when there is the necessity of supporting adequate decision-making to accelerate evolution across multiple domains, under uncertainty.
This Research Topic aims to stimulate research on a broad range of topics related to energy analytics and informatics, providing insightful analyses that can foster future collaborative exchange among academia, industry, government and civil society. The editors welcome submissions of theoretical and methodological studies, as well as of empirical research focused on practical applications and case studies, or a combination thereof.
Topics of interest include, but are not limited to, the following issues:
• Open data standards and open science practices in energy research
o Open data standards and their implications for energy research
o Open source modelling projects in the energy sector
o Transparency of modelling methodologies and documentation
• Data science and analytics for energy research
o Component level analytics (individual technologies)
o Building system level analytics
o Neighbourhood/city scale level analytics and interaction with infrastructures
• Multi-scale analysis techniques
o Scalability to enable effective transitions of energy systems at multiple scales and use of similar
techniques for multiple purposes
o Hybrid physical/statistical models of energy systems, considering both technical/physical factors
and social factors (human influenced)
o Models for vertical and horizontal integration of information
• Insights on the use of multi-scale analysis techniques to support sustainable energy transitions in the
built environment
o Suitability for multiple purposes across building life cycle phases, enabling or supporting innovative
business models aimed at efficiency and sustainability
o Continuity in the use of multiple types of interconnected models across life cycle phases and
continuous improvement strategies using feedback from data
o Multi-scale applications of energy data analytics in buildings, addressing multiple purposes, spatial
and temporal scales as well as different life-cycle phases
Keywords: Energy modelling, Multi-scale analysis, Internet of Things (IoT), Cyber-Physical Systems (CPS), Uncertainty in modelling, Open data, Transparency and reproducibility
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
