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

• Published in: Applied energy 2021-12, Vol.304, p.117798, Article 117798
• Main Authors: Haben, Stephen, Arora, Siddharth, Giasemidis, Georgios, Voss, Marcus, Vukadinović Greetham, Danica
• Format: Article
• Language: English
• Subjects: Demand forecasting; Load forecasting; Low voltage; Machine learning; Neural networks; Review; Smart grid; Smart meter; Substations; Survey; Time series
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2021.117798

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. •Literature review on the topic of Low Voltage (LV) load forecasting.•Overview of current approaches, core applications, datasets, trends and challenges.•Focus is on hierarchy between household-level and system-level load forecasting.•A set of recommendations, which can serve as best practices, are provided.•Future work identified for the continued improvement and advances in this area.