Identification of user requirements for an energy scenario database

Energy scenarios assist decision making regarding the transformation of the energy supply system. A multitude of scenarios exists in various formats. Thus, for scientists and policy stakeholders alike, it remains difficult to distinguish and compare scenario data. Hence, the aim of the project SzenarienDB is to establish an energy scenario database containing data in comparable and machine-readable format. SzenarienDB will do so by extending the OpenEnergyPlatform (OEP). To ensure that the extension fulfils the requirements of the modelling community, we conducted an online survey. We asked the participants about what they expected of an energy scenario database. Along with input from expert meetings and GitHub issues on that topic, we derived user requirement from the answers. In total, we identified 69 requirements. Out of these, around 44% were considered as very urgent. Hence, we conclude that there is a great need for the development of a consistent energy scenario database. To tackle the requirements we grouped these into twelve categories: input and output, data review process, bug-fixes, documentation, factsheets, features, functions to modify data, layout, metadata, ontology, references, and other. Each category is resolved according to its intrinsic properties

Introduction and evaluation of the Open Energy Ontology (OEO)

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Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis

Heterogeneous data, different definitions and incompatible models are a huge problem in many domains, with no exception for the field of energy systems analysis. Hence, it is hard to re-use results, compare model results or couple models at all. Ontologies provide a precisely defined vocabulary to build a common and shared conceptualisation of the energy domain. Here, we present the Open Energy Ontology (OEO) developed for the domain of energy systems analysis. Using the OEO provides several benefits for the community. First, it enables consistent annotation of large amounts of data from various research projects. One example is the Open Energy Platform (OEP). Adding such annotations makes data semantically searchable, exchangeable, re-usable and interoperable. Second, computational model coupling becomes much easier. The advantages of using an ontology such as the OEO are demonstrated with three use cases: data representation, data annotation and interface homogenisation. We also describe how the ontology can be used for linked open data (LOD)