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

Stephen Haben, Siddharth Arora, Georgios Giasemidis, Marcus Voss and Danica Vukadinović Greetham

Applied Energy, 2021, vol. 304, issue C, No S0306261921011326

Abstract: 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.

Keywords: Low voltage; Smart meter; Load forecasting; Demand forecasting; Substations; Smart grid; Machine learning; Time series; Neural networks; Review; Survey (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (20)

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DOI: 10.1016/j.apenergy.2021.117798

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