4.7 Article

Open data and energy analytics - An analysis of essential information for energy system planning, design and operation

Journal

ENERGY
Volume 213, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.118803

Keywords

Open energy data; Energy modelling techniques; Multi-energy systems; Multi-scale analytics; Energy analytics

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Energy transitions are reshaping global and national energy systems and appropriate decision-making strategies are needed to drive an effective change in response to pressing global issues. Governmental institutions, industry, academia, and civil society are all participating to this global change, playing different roles. Open energy models and associated data are essential to promote open science practices, and create an effective science-policy interaction. For example, they can foster multi-disciplinary research addressing the co-evolution of energy technologies and human behaviour more transparently and, more in general, they can improve the interaction of multiple linked models and data, by improving them with respect to the current state of the art. In this paper, we present an analysis of features of open energy models and data, highlighting essential information that can be shared among communities of researchers in the energy field to foster multidisciplinary research. This information inherently embodies different key concepts and perspectives in modelling that affect both simulation and optimization processes employed for energy systems planning, design and operation. Indeed, this shared knowledge is crucial to overcome critical technical issues (e.g. end-use energy efficiency improvements, energy con version processes, energy infrastructures operation, etc.) that may inhibit successful energy transitions. Finally, ecosystems of interacting open data and models are key assets for the development of next generation energy services and technologies, based on innovative business models in which the problem of monitoring, verifying and tracking performance transparently (at multiple levels) will be fundamental. (C) 2020 Elsevier Ltd. All rights reserved.

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