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

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Hayes, Barry P.
Gruber, Jorn K.
Prodanovic, Milan
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Institution of Engineering and Technology (IET)
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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.
Distributed power generation , Load forecasting , Smart meters , 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 , Power system measurement and metering , Power system planning and layout
Hayes, B. P., Gruber, J. K. and Prodanovic, M. (2018) 'Multi-nodal short-term energy forecasting using smart meter data', IET Generation, Transmission & Distribution, 12(12), pp. 2988-2994. doi: 10.1049/iet-gtd.2017.1599
© The Institution of Engineering and Technology 2018. This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission and Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library