A Platform Ecosystem Providing New Data For The Energy Transition

Abstract

There is a great need for high-quality and comprehensive data in the energy sector. This data is collected and preprocessed at considerable expense and is not only required for research, but also by planning offices and other industries in connection with planning activities, such as the creation of municipal heat planning. The NEED ecosystem will accelerate these processes establishing an efficient, robust, and scalable energy data ecosystem. Heterogeneous energy-related data sources will be brought together and automatically linked consistently across different sectors as well as temporal and spatial levels. In this context, existing data sources will not be replaced but rather integrated into the NEED ecosystem as dedicated sources including a semantic description on how to utilize them. In addition to conventional data sources from the various planning levels, we envision a quality assessment scheme based on the FAIR criteria. In reality, we are often faced with missing data, too. To close this gap we explore data-driven, model-driven, AI-based, and tool-driven generation of synthetic data. These heterogeneous data sources will be interlinked using ontology modules which will be represented in a knowledge graph. Via a semantic API, queries will be generated to identify the required data sources, which will be orchestrated to provide the data needed. This will enable researchers, planners, and others including their tools to interact with the NEED ecosystem, while a tool proxy will be able to translate the resulting data into proprietary formats, required by some tools to operate. The NEED ecosystem is planned to be a robust, easy-to-maintain, and flexible infrastructure to enhance planning energy measures at different spatial levels and with different time horizons. We envision to evaluate our NEED approach for the transparent provision of data by integrating relevant data sources as microservices, definition and analysis of application scenarios in the planning domain, as well as the integration of various tools for different planning purposes. With these elements, we will be able to quantify the efficiency of data procurement and demonstrate the functionality of the approach using practical use cases.