Review of low voltage load forecasting: Methods, applications, and recommendations

被引:82
作者
Haben, Stephen [1 ]
Arora, Siddharth [1 ]
Giasemidis, Georgios
Voss, Marcus [2 ]
Greetham, Danica Vukadinovic [3 ]
机构
[1] Univ Oxford, Oxford, England
[2] Tech Univ Berlin DAI Lab, Berlin, Germany
[3] Tessella, Abingdon, Oxon, England
关键词
Low voltage; Smart meter; Load forecasting; Demand forecasting; Substations; Smart grid; Machine learning; Time series; Neural networks; Review; Survey; DISTRIBUTION FEEDER RECONFIGURATION; MODEL-PREDICTIVE CONTROL; SMART METER DATA; RESIDENTIAL ELECTRICITY; DISTRIBUTION-SYSTEMS; HOUSEHOLD-LEVEL; PEAK REDUCTION; BIG DATA; TERM; DEMAND;
D O I
10.1016/j.apenergy.2021.117798
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:26
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