Multi-nodal short-term energy forecasting using smart meter data

作者：Hayes Barry P^*; Gruber Jorn K; Prodanovic Milan

来源：IET Generation Transmission & Distribution, 2018, 12(12): 2988-2994.

DOI：10.1049/iet-gtd.2017.1599

• smart meters
• load forecasting
• distributed power generation
• multinodal short-term energy forecasting
• smart meter data
• advanced metering infrastructure
• AMI
• electrical energy demand modelling
• multinodal demand forecasting
• distributed energy system
• microgrid
• transactive energy
• European country

This paper deals with the short-term forecasting of electrical energy demands at the local level, incorporating advanced metering infrastructure (AMI), or smart meter' data. It provides a study of the effects of aggregation on electrical energy demand modelling and multi-nodal demand forecasting. This paper then presents a detailed assessment of the variables which affect electrical energy demand, and how these effects vary at different levels of demand aggregation. Finally, this study outlines an approach for incorporating AMI data in short-term forecasting at the local level, in order to improve forecasting accuracy for applications in distributed energy systems, microgrids and transactive energy. The analysis presented in this study is carried out using large AMI data sets comprised of recorded demand and local weather data from test sites in two European countries.

• 出版日期2018-7-10