RELIABLE DATA PROFILING FOR ENERGY COMMUNITIES - REVIEW OF OPEN-SOURCE APPROACHES

被引:1
|
作者
Kairisa, E. [1 ]
Mutule, A. [2 ]
机构
[1] Riga Tech Univ, Inst Power Engn, Riga, Latvia
[2] Smart Grid Res Ctr, Inst Phys Energet, Riga, Latvia
关键词
Energy community; electric load; renewable energy; profiling; modeling; policy; open-source data; open-source tools; BENEFITS; BARRIERS; UK;
D O I
10.2478/lpts-2023-0008
中图分类号
O59 [应用物理学];
学科分类号
摘要
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.
引用
收藏
页码:17 / 30
页数:14
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