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• W4365813637 abstract "Abstract Meeting the challenges of the energy sector relies on data – in particular sharing it internally and externally with a wide range of partners. Unfortunately, this valuable data often cannot be obtained from real objects due to location specifics or privacy concerns, although accurate, open-source data are a priority to provide researchers and energy experts with the information needed to accelerate the energy transition. In recent years, many studies have focused on the development of energy communities, using different methods to create data for case studies; however, these methods are often too broad and do not correlate with conditions in real locations. This work aims to identify the challenges associated with creating realistic datasets for energy community studies, as well as highlight the methods of defining input data, considering the factors that make energy community studies a very complex task, and discuss the flaws of commonly used methods." @default.
• W4365813637 created "2023-04-16" @default.
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• W4365813637 date "2023-04-01" @default.
• W4365813637 modified "2023-09-25" @default.
• W4365813637 title "Reliable Data Profiling for Energy Communities – Review of Open-Source Approaches" @default.
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• W4365813637 doi "https://doi.org/10.2478/lpts-2023-0008" @default.
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