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Data-driven management and analytics for green energy production

The aim of this thesis is to devise a technique for data collection, enrichment, integration, storage, and access for data in the area of green energy production focusing on solar panel data.

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Keywords: Green energy, Solar panels, Big data pipelines, Data collection, Datasets

The thesis will include the development of a comprehensive process for data collection, enrichment, integration, storage and access making use of big data management technologies for solar panel systems and battery storage. Data collection, involves the acquisition of raw data from various sources within the solar panel system and battery storage. This includes yield data and multi-sensory data such as operational, thermal, mechanical and environmental data. Collected data is then enriched by integrating with external data sources such as energy price data, weather forecast data and adding context and details. Integration follows, sensor-data-image fusion where data from multiple sources is unified into a consistent structure, offering a comprehensive view of the system's performance. These data are then securely stored in databases or cloud systems, ensuring easy access, retrieval, and data integrity for future analysis. Furthermore, real-time data processing and decision making is facilitated through deploying edge-to-cloud communication, synchronization methods between edge and cloud data, and processing data on the edge devices.

Work to be done:

  • State-of-the-art comparison of available solar panel data systems.
  • Development of data pipelines for ingesting, harmonizing, and integrating solar panel related data.
  • Creation and publication of datasets.