期刊
APPLIED ENERGY
卷 304, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.117798
关键词
Low voltage; Smart meter; Load forecasting; Demand forecasting; Substations; Smart grid; Machine learning; Time series; Neural networks; Review; Survey
资金
- German Federal Ministry for Economic Affairs and Energy (BMWi) [03SIN539]
The increased digitalisation and monitoring of the energy system offer numerous opportunities for decarbonisation, especially through applications on low voltage, local networks. Reliable forecasting is crucial for these systems to anticipate key features and uncertainties. This paper aims to provide an overview of the current landscape, challenges, and recommendations for low voltage level forecasts to facilitate further research and development.
The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.
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